Technology Support I - Java/Python, UI, AWS, LLM

📍 Job Overview

Job Title: Technology Support I - Java/Python, UI, AWS, LLM Company: JPMorgan Chase & Co. Location: Bengaluru, Karnataka, India Job Type: Full time Category: Technology Support / Engineering Date Posted: February 12, 2026 Experience Level: Entry-level (0-2 years) Remote Status: On-site

🚀 Role Summary

• This role focuses on providing Level 3 (L3) technical support for production systems, specifically those leveraging Large Language Models (LLMs), within the Commercial & Investment Bank's Markets Tech team.
• It involves the design, development, and troubleshooting of LLM-powered applications and services, including areas like retrieval-augmented generation (RAG), agent workflows, and structured data extraction.
• A key aspect is ensuring the operational stability, availability, and performance of critical application flows, with a strong emphasis on proactive issue identification and resolution to guarantee a seamless user experience.
• The position requires hands-on coding in Java and Python to build LLM-enabled microservices, inference pipelines, and data tooling, while also managing data quality rules and guardrails for LLM outputs.

📝 Enhancement Note: While titled "Technology Support I," the description details advanced responsibilities in LLM application development, system design, and production support, suggesting a role that bridges traditional support with specialized AI/ML engineering tasks. The focus on "creative LLM assisted software solutions" and "agentic AI" indicates a forward-looking role within a major financial institution.

📈 Primary Responsibilities

• Execute creative LLM-assisted software solutions, including designing, developing, and troubleshooting LLM-powered applications and services such as retrieval-augmented generation (RAG), agent workflows, structured extraction, and classification, with a focus on novel agentic AI approaches.
• Develop and enforce data quality rules and controls using LLMs, defining and implementing guardrails for prompts, retrieved context, model inputs/outputs, and post-processing, including PII redaction, toxicity/safety filters, hallucination mitigation, output schema validation, and policy compliance.
• Provide Level 3 (L3) support for LLM-assisted production systems, taking ownership of complex incidents, managing model and prompt rollouts/rollbacks, resolving dependency issues (e.g., vector stores, embeddings, feature stores), and ensuring high availability, reliability, and adherence to Service Level Agreements (SLAs), including latency and cost budgets.
• Support Business As Usual (BAU) operations for Markets businesses by maintaining and evolving LLM use cases that support markets workflows, employing disciplined change management, canary releases, A/B tests, and close partnerships with product, controls, and operations teams.
• Create secure, high-quality production code by implementing LLM-assisted microservices, synchronous and asynchronous inference pipelines (streaming where appropriate), deterministic fallbacks, circuit breakers, and robust observability for production reliability.
• Produce architecture and design artifacts, deliver model cards, system/data lineage documentation, RAG/agent reference architectures, prompt libraries and versioning strategies, and evaluation plans, ensuring design constraints and regulatory expectations are met during development.
• Identify hidden problems and patterns by utilizing telemetry, error analysis, prompt and context analytics, and drift detection to enhance model selection, prompt strategies, retrieval quality, chunking/embedding strategies, and overall system architecture.
• Drive LLM Ops best practices by integrating models, prompts, and evaluations into CI/CD pipelines, enforcing approvals, segregation of duties, and reproducibility, automating regression and guardrail tests, and managing the lifecycle across environments.
• Ensure a deep understanding of the strengths, limitations, and risk characteristics of approved LLMs (e.g., Claude, Chat GPT, and successor models), including safety profiles, context limits, determinism strategies, and fine-tuning vs. prompt-only tradeoffs, and design multi-agent workflows that incorporate LLM-driven analysis, code generation, testing, and review with explicit human approval gates and segregation of duties.
• Ensure LLM-driven systems meet enterprise reliability and resilience expectations, including disaster recovery, fallback behaviors, regional resiliency, and performance Service Level Objectives (SLOs).

📝 Enhancement Note: The responsibilities emphasize a blend of advanced software engineering, cutting-edge AI/LLM development, and critical production support within a highly regulated financial environment. The focus on "agentic AI," "guardrails," "LLMOps," and "disaster recovery" highlights the complexity and criticality of the role, requiring a candidate with a strong technical foundation and an understanding of operational resilience.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's degree in Computer Science, Engineering, or a related technical field is typically expected for roles involving advanced programming and system design in financial institutions. Experience: 1+ years of experience or equivalent expertise in troubleshooting, resolving, and maintaining information technology services.

Required Skills:

• Strong coding skills in Java, Python, and SQL, specifically applied to building LLM-enabled microservices, retrieval pipelines, evaluators, and data tooling.
• Solid understanding of data structures, algorithms, and object-oriented programming, with an emphasis on how these apply to LLM latency, caching, and throughput optimization.
• Hands-on experience with AWS and cloud data management services (e.g., Redshift, Dynamo DB, Aurora, Databricks).
• Experience integrating managed model endpoints and embedding/vector services, including familiarity with secure secret management, networking, and least-privilege access principles.
• Proficiency in automation, CI/CD (Continuous Integration/Continuous Deployment), and Agile methodologies, with specific extensions for LLMOps, including prompt and configuration versioning, automated evaluations, canary releases, and rollback strategies.
• Experience in system design, application development, and ensuring operational stability for LLM architectures, including retrieval layers, vector stores, caching mechanisms, observability, rate limiting, and backpressure strategies.
• Strong analytical, problem-solving, and communication skills, with the ability to clearly explain complex model behaviors, tradeoffs, and control decisions to both technical and non-technical stakeholders.
• Expert-level knowledge of how large language models work and hands-on experience training and fine-tuning approved models (e.g., Claude, Chat GPT, and successors).
• Proven track record integrating LLMs as controlled, reliable components of the software engineering lifecycle in regulated environments, ensuring determinism, reproducibility, safety, and traceability.
• Strong understanding of data modeling challenges in big data and LLM contexts, including embeddings, chunking strategies, vector similarity nuances, retrieval quality measures, and document lineage.
• Ability to provide L3 and BAU support for Markets by leveraging LLMs for incident triage, run book retrieval, and pre-approved auto-remediation, including on-call coverage for LLM services and dependencies.

Preferred Skills:

• Experience defining model usage guidelines, outlining appropriate models for requirements analysis, code generation and refactoring, test generation, documentation, and explanation.
• Ability to lead the use of LLMs for structured requirements analysis, translating business and regulatory requirements into clear technical specifications and control implementations.
• Experience establishing best practices for prompt-driven design and development, treating prompts and system instructions as versioned, reviewable engineering artifacts with change control and traceability.
• Ensuring prompt strategies support determinism, reproducibility, and traceability in regulated environments (e.g., seeded examples, constrained decoding, output schemas, and canonical evaluation sets).
• Overseeing prompt libraries and reusable patterns aligned with enterprise coding and architectural standards, including shared retrieval components and guardrail policies.
• Ability to continuously learn new developments in Agentic AI and LLM-driven software coding.

📝 Enhancement Note: The required skills highlight a demand for engineers who are proficient in established programming languages (Java, Python) and cloud platforms (AWS) but also possess specialized expertise in the rapidly evolving field of Large Language Models and AI operations (LLMOps). The emphasis on regulated environments and traceability is critical for financial services.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate practical application of Java, Python, and SQL in building functional microservices, data pipelines, or analytical tools, ideally showcasing experience with APIs and data integration.
• Provide examples of system design or architecture artifacts for applications, particularly those involving cloud components or complex data flows, highlighting considerations for scalability, reliability, and security.
• Include case studies or project descriptions that illustrate troubleshooting complex technical issues, detailing the problem, the methodology used for diagnosis, and the resolution implemented.
• Showcase experience with CI/CD pipelines, automation scripts, or agile development processes, demonstrating an understanding of efficient software development lifecycles.

Process Documentation:

• Documentation of workflows or processes for managing production incidents, including steps for triage, escalation, resolution, and post-incident review, with an emphasis on maintaining service availability and meeting SLAs.
• Examples of defining and implementing data quality rules, validation checks, or operational controls for applications, particularly where LLMs are involved in data processing or decision-making.
• Evidence of system monitoring, logging, and observability practices, including the use of specific tools or techniques to track application performance, identify anomalies, and ensure system health.
• Any documentation related to developing or managing LLM-specific processes, such as prompt engineering guidelines, model evaluation frameworks, or LLMOps practices for deployment and version control.

📝 Enhancement Note: For this role, a portfolio should not only reflect coding proficiency but also demonstrate a strong understanding of operational resilience, system design, and the emerging practices within LLMOps. Case studies showcasing problem-solving in production environments, especially those involving complex systems or data integrity, will be highly valued.

💵 Compensation & Benefits

Salary Range: For an entry-level (0-2 years experience) Technology Support I role with specialized skills in Java, Python, AWS, and LLMs in Bengaluru, India, a competitive salary range would typically fall between ₹8,00,000 to ₹15,00,000 per annum. This range can vary based on the candidate's specific skills, interview performance, and the exact scope of responsibilities.

Benefits:

• Comprehensive health insurance coverage, including medical, dental, and vision plans.
• Retirement savings plan (e.g., Provident Fund) with company contributions.
• Paid time off, including vacation days, sick leave, and public holidays.
• Opportunities for professional development, including training programs, certifications, and access to learning resources related to AI, LLMs, and cloud technologies.
• Potential for performance-based bonuses and annual increments.
• Employee assistance programs offering confidential counseling and support services.
• Access to company-provided amenities and facilities at the Bengaluru office.

Working Hours: The standard working hours are 40 hours per week, typically from Monday to Friday, aligning with business needs in the Indian market. However, the role may require on-call availability for critical incident support outside of standard business hours, as indicated by the need for "on-call coverage for LLM services and dependencies."

📝 Enhancement Note: The salary estimate is based on industry benchmarks for similar technical support and junior engineering roles in Bengaluru, considering the specialized skills in LLMs and cloud technologies. The benefits package is standard for large multinational corporations like JPMorgan Chase & Co. in India.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services / Banking Technology. JPMorgan Chase & Co. is a global leader in financial services, operating at the intersection of finance and technology. This specific role is within the Markets Tech team of the Commercial & Investment Bank, focusing on cutting-edge technology solutions for trading and investment operations. Company Size: JPMorgan Chase & Co. is a very large, multinational corporation with tens of thousands of employees globally. This scale implies robust processes, extensive resources, and significant opportunities but also a structured and often matrixed organizational environment. Founded: JPMorgan Chase & Co. has a history spanning over 200 years, with its current form resulting from various mergers, the most significant being the 2000 merger of Chase Manhattan Corporation and J.P. Morgan & Co. This long history signifies stability, deep industry knowledge, and a commitment to long-term strategic growth.

Team Structure:

• The role is within the "Markets Tech" team, part of the Commercial & Investment Bank's technology division. This team likely comprises software engineers, system administrators, data analysts, and specialized AI/ML engineers focused on supporting trading and investment platforms.
• The team structure is likely hierarchical, with clear reporting lines to a team lead or manager, and potentially matrixed reporting for project-specific work. Collaboration with product managers, business analysts, and other technology groups is expected.
• Cross-functional collaboration is essential, involving close partnerships with product teams, business stakeholders within Markets, controls and risk management departments, and other IT operations teams to ensure the seamless functioning of LLM-supported systems.

Methodology:

• Data analysis and insights methods will be crucial for monitoring system performance, identifying issues, and understanding user behavior with LLM applications. This includes leveraging telemetry, logs, and specialized AI/ML analytics.
• Workflow planning and optimization strategies will be applied to the development, deployment, and maintenance of LLM services, incorporating Agile principles and LLMOps best practices for efficiency and reliability.
• Automation and efficiency practices are paramount, from CI/CD pipelines for code deployment to automated incident response mechanisms and self-service tools for users, all aimed at improving operational throughput and reducing manual intervention.

Company Website: https://www.jpmorganchase.com/ (The provided URL https://jpmc.fa.oraclecloud.com:443/hcmUI/CandidateExperience/ is for their careers portal.)

📝 Enhancement Note: Working in a large, established financial institution like JPMorgan Chase means operating within a highly regulated and secure environment. The Markets Tech team likely emphasizes rigor, compliance, and robust engineering practices, especially when dealing with cutting-edge technologies like LLMs. The company culture values stability, innovation within defined boundaries, and strong operational discipline.

📈 Career & Growth Analysis

Operations Career Level: This role is an "Entry-level" (0-2 years) "Technology Support I" position. However, the specific responsibilities involving LLM development, troubleshooting, and production support elevate it beyond a typical basic support role. It represents an opportunity to gain specialized, high-demand expertise in AI/ML operations within the financial sector. Reporting Structure: The candidate will likely report to a Technology Support Lead or a Manager within the Markets Tech team. This manager will oversee the team's operational efficiency, project delivery, and individual career development. The role will involve close collaboration with senior engineers and product specialists. Operations Impact: This role directly impacts the operational stability and performance of critical systems used in financial markets. By ensuring the availability and reliability of LLM-powered applications, the candidate contributes to the efficiency of trading operations, risk management, and client service delivery, ultimately supporting the revenue-generating activities of the Commercial & Investment Bank.

Growth Opportunities:

• Specialization in AI/ML Operations (LLMOps): Deepen expertise in LLM integration, prompt engineering, model deployment, monitoring, and lifecycle management within a regulated financial environment, becoming a subject matter expert in this niche field.
• Transition to AI/ML Engineering: With proven success and further development, opportunities may arise to move into full AI/ML Engineering roles, focusing more on model development, fine-tuning, and advanced AI architecture design.
• Cross-functional Exposure: Gain exposure to various financial market workflows and technologies, enabling a broader understanding of the business and opportunities to contribute to different technology domains within the bank.
• Leadership Potential: Develop leadership skills through incident management, mentoring junior team members, and potentially leading small projects or feature rollouts, paving the way for team lead or management positions.

📝 Enhancement Note: While starting as a support role, the advanced technical requirements and focus on emerging AI technologies offer significant growth potential. Candidates who excel can leverage this experience to move into specialized engineering tracks within AI/ML or pursue broader technology leadership roles within the financial services industry.

🌐 Work Environment

Office Type: The role is designated as "On-site" in Bengaluru, India. This suggests a traditional office environment within JPMorgan Chase's corporate facilities, designed to foster collaboration and adherence to security protocols. Office Location(s): The specified location is GR. FLR., 1ST TO 6TH FLR., PLATINA, BLOCK-3, KODBISANHALLI, OUTER RING , ROAD, BANGALORE EAST TAL., Bengaluru, India. This is a modern office complex in a key business district of Bengaluru.

Workspace Context:

• The workspace will likely be a collaborative office setting, encouraging interaction with team members and fostering a culture of knowledge sharing, particularly around complex technical challenges and LLM implementations.
• Access to robust IT infrastructure, development tools, and potentially specialized hardware or cloud environments necessary for working with LLMs and large datasets will be provided.
• Opportunities for direct interaction with peers, senior engineers, and potentially business stakeholders will be frequent, facilitating rapid learning and problem-solving.

Work Schedule: The standard work schedule is likely 40 hours per week, typically Monday to Friday. However, the "on-call coverage" requirement for LLM services and dependencies indicates that flexibility may be needed to address critical production issues outside of regular business hours to ensure 24/7 operational stability for financial markets.

📝 Enhancement Note: The on-site requirement is typical for roles in financial institutions where data security and infrastructure access are paramount. The emphasis on collaboration within a modern office setting, combined with the need for on-call support, defines the day-to-day operational rhythm.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or HR representative will likely conduct an initial screening to assess basic qualifications, interest in the role, and alignment with company culture.
• Technical Assessment (Coding Challenge): Candidates can expect a coding assessment, likely focusing on Java/Python, SQL, and potentially basic algorithmic problems. Given the role's LLM focus, there might be questions or a small exercise related to API interaction or data manipulation.
• Technical Interview(s): Multiple rounds of technical interviews will delve deeper into the candidate's proficiency in Java, Python, SQL, AWS, and general system design. Expect questions on data structures, algorithms, troubleshooting methodologies, and cloud concepts. Specific questions on LLM concepts, RAG, agent workflows, and LLMOps might be included.
• Behavioral Interview: This round assesses cultural fit, problem-solving approach, teamwork, communication skills, and how the candidate handles pressure or challenging situations, particularly relevant for a support role. Questions may focus on past experiences with incident management or complex technical problem-solving.
• Hiring Manager Interview: A final interview with the hiring manager to discuss career aspirations, team fit, and confirm overall suitability for the role and company.

Portfolio Review Tips:

• Showcase Relevant Technologies: Highlight projects demonstrating proficiency in Java, Python, SQL, and AWS. If possible, include any personal or professional projects involving LLM APIs, data processing pipelines, or cloud-based deployments.
• Emphasize Problem-Solving: For each project, clearly articulate the problem you solved, your approach, the technologies used, and the outcome. Quantify results where possible (e.g., "reduced processing time by X%," "resolved Y critical bugs").
• Document System Design: If you have designed systems, include diagrams or descriptions that illustrate your understanding of architecture, scalability, and reliability, especially for cloud-native or microservices-based applications.
• Detail Troubleshooting Experience: Prepare specific examples of complex technical issues you've diagnosed and resolved. Explain your systematic approach to troubleshooting.
• LLM/AI Exposure: Even if limited, any exposure to LLMs, AI concepts, or data science projects should be clearly presented. Highlight your understanding of their potential applications and challenges.

Challenge Preparation:

• Coding Practice: Focus on LeetCode-style problems for Java and Python, covering data structures (arrays, linked lists, trees, hash maps) and algorithms (sorting, searching, dynamic programming). Practice SQL queries for data retrieval and manipulation.
• AWS Fundamentals: Review core AWS services relevant to development and operations (EC2, S3, RDS, Lambda, CloudWatch). Understand concepts like VPCs, security groups, and IAM.
• LLM Concepts: Familiarize yourself with fundamental LLM concepts: tokenization, embeddings, transformers, prompt engineering, RAG, agentic workflows, hallucinations, and common LLMs (GPT, Claude). Understand the basics of vector databases.
• LLMOps: Research best practices for deploying, monitoring, and managing LLMs in production, including version control for prompts, automated testing, and feedback loops.
• Behavioral Responses: Prepare STAR method (Situation, Task, Action, Result) responses for common behavioral questions related to teamwork, problem-solving, handling pressure, and learning from mistakes.

📝 Enhancement Note: The interview process is designed to rigorously assess both technical depth in core programming and cloud technologies, as well as specialized knowledge in the rapidly evolving field of LLMs and AI operations. A strong portfolio showcasing practical application of these skills, coupled with well-prepared answers to technical and behavioral questions, will be key to success.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Java, Python, SQL. Proficiency in these is essential for development and scripting.
• Cloud Platform: AWS (Amazon Web Services). Expect usage of services for compute (EC2, Lambda), storage (S3), databases (RDS, DynamoDB), and potentially AI/ML services.
• LLM Frameworks/Libraries: Experience with libraries for interacting with LLMs (e.g., LangChain, LlamaIndex) and potentially model inference frameworks.
• Version Control: Git (e.g., GitHub, GitLab, Bitbucket) for code management and collaboration.
• CI/CD Tools: Jenkins, GitLab CI, AWS CodePipeline, or similar for automating build, test, and deployment processes.

Analytics & Reporting:

• Monitoring & Observability: Tools like CloudWatch, Prometheus, Grafana, Datadog, or Splunk for tracking application performance, logs, and system health.
• Data Warehousing/Databases: Experience with relational databases (e.g., PostgreSQL, Oracle) and potentially NoSQL databases (e.g., DynamoDB) or data warehouses (e.g., Redshift, Snowflake).
• Vector Stores: Familiarity with specialized databases for storing and querying vector embeddings (e.g., Pinecone, Weaviate, Chroma DB, or AWS OpenSearch with vector capabilities).

CRM & Automation:

• While not explicitly mentioned, familiarity with IT Service Management (ITSM) tools (e.g., ServiceNow) for incident management and ticketing is highly probable in a support role.
• Scripting for automation tasks within the AWS environment or for operational workflows.

📝 Enhancement Note: The technology stack emphasizes a modern cloud-native development and operations environment, with a strong focus on AWS and the integration of LLM technologies. Candidates should be prepared to demonstrate hands-on experience with these tools and an understanding of how they interoperate within a large enterprise setting.

👥 Team Culture & Values

Operations Values:

• Reliability and Stability: A core value in financial services, ensuring systems are always available, performant, and secure is paramount. This translates to rigorous testing, robust error handling, and diligent monitoring.
• Innovation with Control: While encouraging the adoption of new technologies like LLMs, there's a strong emphasis on controlled implementation, risk mitigation, and compliance with regulatory requirements.
• Data-Driven Decision Making: Utilizing telemetry, logs, and analytics to understand system behavior, diagnose issues, and guide improvements is fundamental.
• Efficiency and Automation: A drive to automate repetitive tasks, optimize workflows, and improve operational throughput through smart technology solutions.
• Collaboration and Communication: Working effectively across teams, sharing knowledge, and communicating complex technical information clearly to diverse stakeholders.

Collaboration Style:

• Cross-functional Integration: Operations teams work closely with development, product, and business units to ensure alignment and address needs holistically. This involves active participation in planning, design reviews, and incident response.
• Process Review and Feedback: A culture of continuous improvement where processes are regularly reviewed, and feedback is encouraged to enhance efficiency and effectiveness. This includes post-incident reviews and retrospectives.
• Knowledge Sharing: Openness to sharing technical knowledge, best practices, and lessons learned through documentation, internal training sessions, and peer-to-peer support.

📝 Enhancement Note: The culture at JPMorgan Chase, particularly within its technology divisions, emphasizes a blend of innovation and stringent control, reflecting the demands of the financial industry. Success in this role will require a proactive, detail-oriented approach, a commitment to operational excellence, and strong interpersonal skills for effective collaboration.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving LLM Landscape: Keeping pace with the fast-changing advancements in LLM technology, new models, and evolving best practices for their integration and management.
• Balancing Innovation with Regulation: Implementing cutting-edge AI technologies while adhering to strict financial regulations, data privacy laws, and internal compliance policies.
• Production Stability of LLMs: Ensuring the reliability, determinism, and safety of LLM-powered applications in a high-stakes production environment, mitigating risks like hallucinations or biased outputs.
• Incident Management Complexity: Troubleshooting intricate issues that span multiple components, including LLM models, data pipelines, AWS infrastructure, and application logic, often under pressure.
• Scale and Performance: Optimizing LLM applications for low latency and high throughput to meet the demands of financial markets, which can be resource-intensive.

Learning & Development Opportunities:

• Specialized LLM Training: Access to internal and external training programs focused on advanced LLM concepts, prompt engineering, LLMOps, and AI ethics.
• Cloud Certifications: Opportunities to pursue AWS certifications (e.g., AWS Certified Developer, AWS Certified Solutions Architect) to deepen cloud expertise.
• Industry Conferences: Potential to attend relevant tech conferences and workshops focused on AI, machine learning, and financial technology.
• Mentorship Programs: Benefit from mentorship from senior engineers and AI specialists within JPMorgan Chase, guiding career development and technical skill enhancement.
• Exposure to Diverse Financial Use Cases: Gain deep insights into how AI/LLMs are applied across various financial market functions, from trading analytics to risk assessment and client advisory.

📝 Enhancement Note: The primary challenges revolve around the inherent complexities of integrating novel AI technologies into a highly regulated and performance-critical domain. However, these challenges also present significant growth opportunities for individuals eager to develop expertise in a high-demand area of technology.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex technical problem you encountered in a previous role and how you systematically approached troubleshooting it. What was the outcome?" (Focus on your process, tools used, and how you identified the root cause, especially if it involved distributed systems or data issues.)
• "How would you ensure the reliability and safety of an LLM-powered application deployed in a production financial environment? What specific controls and monitoring would you implement?" (Discuss guardrails, prompt validation, output schema enforcement, latency monitoring, hallucination detection, and fallback mechanisms.)
• "Imagine a scenario where an LLM service you support is experiencing intermittent failures. How would you diagnose the issue, considering potential problems with the LLM itself, the data pipeline, or the AWS infrastructure?" (Detail your diagnostic methodology, including checking logs, metrics, dependencies, and using tools like CloudWatch.)

Company & Culture Questions:

• "Why are you interested in working at JPMorgan Chase, specifically within the Markets Tech team and with LLM technologies?" (Connect your career goals, interest in finance/technology, and passion for AI with the company's mission and the role's specific focus.)
• "How do you approach learning new and rapidly evolving technologies like LLMs?" (Highlight your proactive learning habits, resources you use, and how you apply new knowledge.)
• "Describe a time you had to communicate a complex technical issue to a non-technical audience. How did you ensure they understood?" (Focus on your ability to simplify complex concepts, use analogies, and tailor your communication.)

Portfolio Presentation Strategy:

• Structure Your Examples: For each project presented, follow a clear narrative: Problem -> Your Solution -> Technologies Used -> Outcome/Impact.
• Quantify Achievements: Whenever possible, use numbers to demonstrate the impact of your work (e.g., "improved performance by X%", "reduced error rate by Y%", "handled Z requests per second").
• Highlight LLM Relevance: If you have LLM-related projects, explain the specific LLM capabilities you used (e.g., text generation, summarization, Q&A) and the business value they provided.
• Showcase Technical Depth: Be prepared to discuss the technical details of your projects, including architecture choices, coding patterns, and challenges faced.
• Demonstrate Problem-Solving: Use your portfolio projects as evidence of your analytical and problem-solving skills, particularly in troubleshooting and system design.

📝 Enhancement Note: The interview preparation advice focuses on demonstrating a strong technical foundation in core areas, coupled with a clear understanding and practical application of LLM technologies and LLMOps principles. The ability to articulate problem-solving processes and communicate effectively will be critical.

📌 Application Steps

To apply for this operations position:

• Submit your application through the official JPMorgan Chase careers portal link provided.
• Customize Your Resume: Tailor your resume to highlight keywords and responsibilities mentioned in the job description, especially focusing on Java, Python, SQL, AWS, LLM, Troubleshooting, CI/CD, and any experience with production support or system design. Quantify achievements wherever possible.
• Prepare Your Portfolio: Curate examples of projects that showcase your coding proficiency, cloud experience, and any exposure to LLMs or AI. Be ready to discuss these in detail, focusing on the problem, your solution, and the outcome.
• Practice Technical & Behavioral Questions: Review common interview questions for software support and entry-level engineering roles, paying special attention to LLM-specific concepts and troubleshooting scenarios. Prepare concise, STAR-method answers for behavioral questions.
• Research JPMorgan Chase & Markets Tech: Understand the company's mission, values, and the specific role of the Commercial & Investment Bank's Markets Tech team. Familiarize yourself with the challenges and opportunities in applying AI/LLMs within financial services.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.