Software Engineer - Python, UI, AI, Exp: 4-8 Yrs, Bangalore

📍 Job Overview

Job Title: Software Engineer - Python, UI, AI

Company: Cisco

Location: Bangalore, India

Job Type: Full time

Category: Software Engineering / Data & Analytics

Date Posted: 2026-07-27

Experience Level: Mid-Level (4-8 Years)

Remote Status: On-site

🚀 Role Summary

• Develop and maintain high-performance full-stack applications and scalable microservices within Cisco's enterprise analytics platform.

• Design, build, and optimize robust RESTful APIs and backend services utilizing Python and Java.

• Collaborate cross-functionally to ensure the security, observability, and reliability of integrated systems.

• Contribute to the continuous delivery of high-quality software solutions and influence platform architecture.

• Implement advanced AI/ML capabilities, including RAG systems and LLM integrations, for enhanced data insights and agentic applications.

📝 Enhancement Note: This role is positioned within Cisco's Data & Analytics team, focusing on building an enterprise analytics platform. The responsibilities lean heavily into full-stack development with a significant emphasis on backend services (Python/Java) and modern frontend frameworks (React), alongside emerging AI/ML technologies like RAG and LLMs. The "Software Engineer - Python, UI, AI" title accurately reflects the core technical demands.

📈 Primary Responsibilities

• Design, develop, and maintain robust RESTful APIs and backend services using Python (FastAPI/Flask) and Java (Spring Boot).

• Define and implement API contracts, validation logic, and error-handling standards to ensure seamless system integration and data integrity.

• Develop secure backend solutions leveraging modern authentication protocols such as OAuth 2.0 and JWT, ensuring code quality through rigorous testing and comprehensive observability.

• Collaborate cross-functionally with architects, frontend developers, QA, and DevOps teams to deliver integrated, high-quality backend features that meet business requirements.

• Troubleshoot production incidents, contribute to CI/CD pipelines, automated testing, and release management processes to support a culture of continuous delivery and operational excellence.

• Build and maintain scalable UI applications using React, integrating state management libraries like Redux or React Query for enhanced user experience and performance.

• Work with SQL and relational databases (e.g., PostgreSQL, MySQL, Oracle) or Data Warehouses (e.g., SAP HANA, Snowflake) for efficient data storage and retrieval.

• Implement and manage microservices architecture, distributed systems, and containerization technologies like Docker and Kubernetes.

• Utilize cloud platforms (AWS, Azure, GCP) and message brokers (Kafka, RabbitMQ) for scalable and resilient system design.

• Develop efficient RAG (Retrieval-Augmented Generation) systems and state-management architectures for long-running agent tasks.

• Integrate and build agentic applications or services leveraging Large Language Models (LLMs).

📝 Enhancement Note: The original job description provided detailed responsibilities. This section expands on those by incorporating operations-relevant keywords such as "data integrity," "operational excellence," "continuous delivery," and "scalable UI applications," which are critical for backend and full-stack engineers in enterprise environments. The inclusion of RAG and LLM responsibilities is highlighted due to its growing importance in data analytics and AI-driven platforms.

🎓 Skills & Qualifications

Education:

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. Experience:

• 4-8 years of professional experience in backend software development, with a strong track record of building RESTful services and scalable applications.

• Proven experience in full-stack development, encompassing both backend service development and modern frontend UI implementation. Required Skills:

• Strong programming proficiency in both Python (FastAPI/Flask/Django) and Java (Spring Boot/MVC).

• Proven experience building modern, scalable UI applications with React, including proficiency in state management libraries like Redux or React Query.

• Demonstrable expertise with SQL and relational databases (e.g., PostgreSQL, MySQL, Oracle) or Data Warehouses (e.g., SAP HANA, Snowflake).

• Solid experience with source control management using Git, implementing automated testing frameworks, and adhering to secure coding practices.

• Experience defining and implementing API contracts and robust error-handling mechanisms for seamless system integration.

• Proficiency in developing secure backend solutions using modern authentication protocols like OAuth 2.0 and JWT. Preferred Skills:

• Experience with microservices architecture, distributed systems, and containerization technologies (Docker, Kubernetes).

• Familiarity with cloud platforms such as AWS, Azure, or GCP, and message brokers like Kafka or RabbitMQ.

• Experience implementing CI/CD pipelines using tools like Jenkins or GitHub Actions and utilizing observability tools (Splunk, ELK, Prometheus, Grafana).

• Experience with NoSQL databases (MongoDB, Cassandra, or DynamoDB) and API gateway patterns.

• Experience working in Agile/Scrum development environments with a focus on high availability system design.

• Experience in building efficient RAG (Retrieval-Augmented Generation) systems and state-management architectures for long-running agent tasks.

• Experience in building agentic applications or integrating LLM-based services.

📝 Enhancement Note: The original minimum and preferred qualifications have been structured to clearly differentiate between required and preferred skills. Operations-relevant terms like "data integrity," "system integration," "high availability system design," and "observability tools" have been integrated to align with the context of building robust and scalable enterprise platforms. The AI/ML specific skills are explicitly listed under preferred qualifications.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of developed RESTful APIs and backend microservices, demonstrating robust design principles, efficient data handling, and scalability.

• Examples of full-stack applications built with React, highlighting UI/UX design, state management implementation, and seamless integration with backend services.

• Case studies demonstrating experience with database design and optimization for relational databases (SQL) or data warehouses. Include examples of complex queries or performance tuning.

• Projects that illustrate proficiency in implementing secure coding practices, authentication protocols (OAuth 2.0, JWT), and utilizing source control (Git) effectively.

• Demonstrations of experience with containerization (Docker) and orchestration (Kubernetes), including deployment and management of containerized applications. Process Documentation:

• Workflow designs for CI/CD pipelines, illustrating automation of build, test, and deployment processes.

• Documentation of API contract definitions, including request/response schemas and error code standardization.

• Examples of observability implementation, detailing how logging, monitoring, and alerting mechanisms were set up for backend services and applications.

• Case studies on building RAG systems or integrating LLM-based services, detailing the architecture, data flow, and performance metrics.

📝 Enhancement Note: For a software engineering role with a focus on backend and AI, a portfolio should emphasize technical execution and process. This section outlines specific project types and documentation examples that would demonstrate proficiency in API development, full-stack capabilities, database management, security, containerization, and emerging AI technologies. The focus is on tangible evidence of process implementation and system design.

💵 Compensation & Benefits

Salary Range:

• Estimated Range: ₹15,00,000 - ₹30,00,000 per annum (On-site, Bangalore, India)

• Methodology: This estimate is based on Glassdoor, LinkedIn Salary, and Payscale data for Software Engineers with 4-8 years of experience in Bangalore, India, with a focus on Python, Java, and UI development. The range accounts for variations in specific skill sets, project complexity, and Cisco's compensation structure for similar roles within the region.

Benefits:

• Comprehensive health insurance including medical, dental, and vision coverage.

• Retirement savings plans, including provident fund contributions.

• Paid time off, including vacation days, sick leave, and public holidays.

• Professional development opportunities, including training, certifications, and conference attendance.

• Employee stock purchase programs and performance-based bonuses.

• Access to Cisco's global employee resource networks and affinity groups.

• Relocation assistance may be available for candidates moving to Bangalore. Working Hours:

• Standard full-time working hours are typically 40 hours per week.

• While specific schedules may vary based on team needs and project deadlines, Cisco generally promotes work-life balance.

• Flexibility may be offered for specific tasks or project phases, subject to team and management approval.

📝 Enhancement Note: Salary for this role in Bangalore, India, is estimated based on industry benchmarks for mid-level software engineers with the specified technical skills. Benefits are typical for a large tech company like Cisco, with a focus on comprehensive coverage and professional growth. The working hours are standard but acknowledge potential flexibility.

🎯 Team & Company Context

🏢 Company Culture

Industry: Networking, Telecommunications, Cybersecurity, Cloud Computing, and increasingly AI.

Company Size: Cisco is a large enterprise with over 80,000 employees globally, indicating a structured environment with extensive resources and opportunities for specialization.

Founded: 1984, providing a long history of innovation and market leadership, now actively transforming for the AI era.

Team Structure:

• The Data & Analytics team is described as "high-performing," suggesting a focus on results and efficiency.

• It comprises intelligent, dedicated, and results-driven professionals.

• The team is responsible for building and maintaining an enterprise analytics platform connecting data across the value chain.

• Collaboration is a key aspect, with cross-functional interaction expected with architects, frontend developers, QA, and DevOps. Methodology:

• Data Analysis & Insights: Mission to deliver persona-based, actionable insights through self-service analytics.

• Workflow Planning & Optimization: Enabling digital processes and accelerating business model transformation.

• Automation & Efficiency: Driving operational efficiency and data-driven decision-making at scale.

• Continuous Delivery: Emphasis on building scalable microservices, ensuring security, observability, and reliability through CI/CD pipelines.

Company Website: https://careers.cisco.com/global/en

📝 Enhancement Note: This section synthesizes information from the company description and the job posting to provide context on Cisco's industry position, size, and the specific nature of the Data & Analytics team. The methodology highlights the operational focus on data, efficiency, and continuous improvement, directly relevant to an engineer's role.

📈 Career & Growth Analysis

Operations Career Level:

• This role is classified as a Mid-Level Software Engineer, with 4-8 years of experience required. It represents a significant step beyond entry-level, demanding a solid understanding of software development lifecycle, architectural patterns, and the ability to contribute independently to complex projects. Reporting Structure:

• The role reports into the Data & Analytics team, likely under a Manager or Director of Engineering.

• Close collaboration with architects, frontend engineers, QA, and DevOps teams is expected, indicating a flat or matrixed team structure common in agile development environments. Operations Impact:

• The engineer's work directly impacts Cisco's ability to leverage data for operational efficiency, business model transformation, and data-driven decision-making.

• By building and enhancing the enterprise analytics platform, the role contributes to providing actionable insights that empower self-service analytics and accelerate strategic initiatives across the company.

• Contributions to secure, observable, and reliable systems are crucial for maintaining business continuity and trust in data. Growth Opportunities:

• Specialization: Deepen expertise in Python, Java, React, or emerging AI/ML technologies (RAG, LLMs).

• Architecture: Transition into roles focusing on system design, microservices architecture, and platform scalability.

• Leadership: Potential to move into Senior Software Engineer, Tech Lead, or Engineering Management roles, guiding teams and technical direction.

• Cross-functional Exposure: Gain experience across different functional areas of Cisco's vast technology portfolio through project involvement.

• Continuous Learning: Opportunities to attend conferences, pursue certifications, and engage in internal training programs focused on cutting-edge technologies.

📝 Enhancement Note: This analysis frames the Software Engineer role within a broader operations and career progression context. It emphasizes the impact of engineering contributions on business operations and outlines clear pathways for growth within a large enterprise like Cisco, focusing on skill development and leadership potential.

🌐 Work Environment

Office Type: On-site in Bangalore, India. This suggests a traditional office environment focused on in-person collaboration.

Office Location(s): Bangalore, India (Specific office address not provided but is a major tech hub).

Workspace Context:

• Collaborative Environment: The role explicitly mentions working within a collaborative environment, implying shared workspaces, team meetings, and active participation in group problem-solving.

• Operations Tools & Technology: Access to Cisco's standard development tools, including robust development machines, high-speed network access, and potentially specialized hardware for AI/ML development.

• Team Interaction: Frequent interaction with architects, fellow engineers, QA, DevOps, and potentially product managers, fostering a dynamic and communicative work atmosphere.

Work Schedule:

• Typically 40 hours per week, standard for full-time engineering roles.

• While on-site, there may be opportunities for some schedule flexibility depending on team needs and project phases, encouraging a balance between structured work and efficient task completion.

📝 Enhancement Note: The on-site nature of the role in Bangalore is highlighted. The workspace context emphasizes the collaborative, tech-rich environment expected at a company like Cisco, with a focus on team interaction and the tools necessary for efficient software development and operations.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or Recruiter call to assess basic qualifications, experience, and cultural fit.

• Technical Phone/Video Interview(s): Focused on core programming skills (Python, Java), data structures, algorithms, and system design concepts.

Expect coding challenges.

• On-site/Virtual On-site Interviews: Multiple rounds involving:

  • Coding Assessments: Live coding exercises (e.g., LeetCode style problems, API implementation) to evaluate problem-solving and coding proficiency.
  • System Design Interview: Discussion on designing scalable architectures for microservices, APIs, or AI-driven systems.
  • Behavioral Interview: Questions assessing teamwork, problem-solving approach, communication skills, and alignment with Cisco's values.
  • AI/ML Specific Interview: May include discussions on RAG, LLM integration, and related concepts if the role heavily emphasizes these areas.

• Hiring Manager Discussion: Final conversation to discuss role specifics, team dynamics, and career aspirations.

Portfolio Review Tips:

• Curate Select Projects: Showcase 2-3 of your strongest projects that directly align with the job requirements (Python/Java backend, React UI, AI/ML).

• Highlight Impact: For each project, clearly articulate the problem you solved, your specific contributions, the technologies used, and the measurable outcomes or impact (e.g., performance improvements, scalability achieved, user satisfaction).

• Code Quality: Be prepared to walk through code snippets demonstrating clean architecture, efficient algorithms, proper error handling, and adherence to security best practices.

• System Design Diagrams: If applicable, use diagrams to explain the architecture of your projects, especially for microservices or AI integrations.

• AI/ML Focus: Clearly explain the architecture and implementation of any RAG systems or LLM integrations, including data pipelines, model interactions, and performance metrics.

Challenge Preparation:

• Data Structures & Algorithms: Practice common algorithms and data structure problems regularly.

• System Design: Study common system design patterns (e.g., load balancing, caching, message queues, database scaling) and practice designing for high availability and scalability.

• API Design: Understand RESTful principles, API versioning, authentication, and rate limiting.

• AI/ML Concepts: Refresh knowledge on RAG architecture, LLM fundamentals, prompt engineering, and agentic workflows.

• Behavioral Questions: Prepare STAR method (Situation, Task, Action, Result) responses for common behavioral questions.

📝 Enhancement Note: This section provides detailed, actionable advice for the interview process, tailored to a software engineering role with AI components. It emphasizes portfolio presentation, specific interview types, and preparation strategies, including a focus on AI/ML aspects and system design.

🛠 Tools & Technology Stack

Primary Tools:

• Backend Languages/Frameworks: Python (FastAPI, Flask, Django), Java (Spring Boot, MVC).

• Frontend Frameworks: React (with state management like Redux, React Query).

• Databases: SQL (PostgreSQL, MySQL, Oracle), Data Warehouses (SAP HANA, Snowflake), NoSQL (MongoDB, Cassandra, DynamoDB).

• Containerization & Orchestration: Docker, Kubernetes.

• API Management: API Gateway patterns.

Analytics & Reporting:

• Observability Tools: Splunk, ELK Stack (Elasticsearch, Logstash, Kibana), Prometheus, Grafana for monitoring and logging.

• Data Warehousing Tools: SAP HANA, Snowflake.

CRM & Automation:

• Cloud Platforms: AWS, Azure, GCP.

• Message Brokers: Kafka, RabbitMQ.

• CI/CD Tools: Jenkins, GitHub Actions.

• Source Control: Git.

📝 Enhancement Note: This section lists the specific technologies mentioned in the job description, categorized for clarity. It highlights the core stack (Python, Java, React) and essential supporting technologies like databases, cloud platforms, containerization, and CI/CD tools, crucial for operations professionals to assess their tool proficiency.

👥 Team Culture & Values

Operations Values:

• Results-Driven: Emphasis on delivering high-quality software solutions that accelerate business transformation and drive operational efficiency.

• Innovation: Encouraging the development of new solutions, including those leveraging AI/ML (RAG, LLMs), to stay at the forefront of technology.

• Collaboration: Fostering a team environment where cross-functional work, knowledge sharing, and collective problem-solving are paramount.

• Reliability & Security: A strong commitment to building secure, observable, and reliable systems that maintain data integrity and trust.

• Continuous Improvement: Dedication to practices like CI/CD, automated testing, and iterative development to enhance processes and product quality.

Collaboration Style:

• Cross-functional Integration: Actively working with architects, frontend developers, QA, and DevOps to ensure seamless integration of backend services and features.

• Agile Methodologies: Likely operating within Agile/Scrum frameworks, emphasizing iterative development, regular feedback loops, and adaptability.

• Knowledge Sharing: Encouraging the exchange of technical expertise, best practices, and lessons learned within the team and across departments.

📝 Enhancement Note: This section extrapolates Cisco's general culture and the team's stated mission into specific values and collaboration styles relevant to an engineering role. It emphasizes the operational aspects of quality, efficiency, and continuous improvement.

⚡ Challenges & Growth Opportunities

Challenges:

• Scalability & Performance: Designing and maintaining systems that can handle increasing data volumes and user loads within a large enterprise analytics platform.

• AI/ML Integration Complexity: Successfully implementing and optimizing RAG and LLM-based services, ensuring accuracy, efficiency, and ethical considerations.

• Cross-functional Dependencies: Navigating complex interdependencies between different engineering teams (frontend, backend, DevOps, architects) to deliver integrated features.

• Maintaining High Availability: Ensuring the continuous operation and reliability of critical analytics services, minimizing downtime and impact on business operations.

• Evolving Technology Landscape: Keeping pace with rapid advancements in AI, cloud technologies, and software development practices.

Learning & Development Opportunities:

• AI/ML Specialization: Deep dive into advanced AI/ML concepts, large language models, and retrieval-augmented generation techniques.

• Cloud Architecture: Gaining deeper expertise in designing and deploying solutions on major cloud platforms (AWS, Azure, GCP).

• Microservices & Distributed Systems: Developing advanced skills in building and managing complex, distributed software architectures.

• Leadership Development: Opportunities to mentor junior engineers, lead technical initiatives, and potentially move into team leadership roles.

• Industry Conferences & Certifications: Access to Cisco's resources for attending relevant tech conferences and obtaining industry-recognized certifications.

📝 Enhancement Note: This section identifies potential challenges inherent in the role and the company's domain, framing them as opportunities for skill development and growth. It highlights the exciting, cutting-edge aspects of the role, particularly in AI.

💡 Interview Preparation

Strategy Questions:

• System Design: "Design a scalable microservice architecture for an enterprise analytics platform that ingests data from multiple sources." or "How would you design a system to efficiently serve AI-generated insights from a large knowledge base using RAG?"

  • Preparation: Focus on modularity, scalability, fault tolerance, data pipelines, and integration points. For RAG, discuss vector databases, embedding models, and retrieval strategies.

• Technical Problem Solving: Be prepared for live coding challenges involving algorithms, data structures, and API implementation in Python or Java.

  • Preparation: Practice coding on platforms like LeetCode, HackerRank, focusing on efficiency and clean code.

• Behavioral/Situational: "Describe a time you had to troubleshoot a complex production issue. How did you approach it?" or "Tell me about a challenging cross-functional project and how you managed stakeholder expectations."

  • Preparation: Use the STAR method to structure your answers, highlighting problem-solving skills, communication, and collaboration. Company & Culture Questions:

• "What interests you about Cisco and this specific role within the Data & Analytics team?"

  • Preparation: Research Cisco's recent AI initiatives, its market position, and connect your skills and aspirations to the team's mission of driving data-driven insights.

• "How do you stay updated with the latest trends in software engineering, particularly in AI and cloud technologies?"

  • Preparation: Be ready to discuss specific blogs, conferences, courses, or personal projects that demonstrate your commitment to continuous learning.

• "Describe your experience working in an Agile/Scrum environment."

  • Preparation: Highlight your understanding of Agile principles and your experience with ceremonies like sprint planning, stand-ups, and retrospectives. Portfolio Presentation Strategy:

• Project Selection: Choose projects that best showcase your Python/Java backend, React UI, and any AI/ML (RAG/LLM) experience.

• Narrative Arc: For each project, tell a compelling story: the problem, your solution, your specific contributions, the technologies used, and the impact/results achieved.

• Technical Depth: Be ready to dive into technical details, explain architectural choices, and discuss trade-offs made during development.

• AI/ML Focus: Clearly articulate the architecture and implementation of RAG/LLM systems, demonstrating your understanding of the underlying concepts and practical application. Use diagrams where helpful.

📝 Enhancement Note: This section provides targeted preparation advice, including example questions and strategies for portfolio presentation, specifically tailored to the technical requirements and likely interview process for a role like this at Cisco.

📌 Application Steps

To apply for this Software Engineer position:

• Submit your application through the Cisco Careers portal link provided.

• Portfolio Customization: Tailor your resume and any linked portfolio (e.g., GitHub profile) to prominently feature your experience with Python, Java, React, RESTful APIs, and any AI/ML projects (RAG, LLMs). Highlight quantifiable achievements and contributions.

• Resume Optimization: Ensure your resume clearly states your years of experience (4-8 years), core technical skills, and relevant projects. Use keywords found in the job description.

• Interview Preparation: Practice coding challenges, system design scenarios, and behavioral questions. Prepare to discuss your portfolio projects in detail, emphasizing your role and impact.

• Company Research: Familiarize yourself with Cisco's mission, its current focus on AI, and its Data & Analytics initiatives. Understand how your role contributes to the company's broader objectives.

⚠️ 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.