UI LMTS - AI Engineering

📍 Job Overview

Job Title: UI LMTS - AI Engineering

Company: Salesforce

Location: Bengaluru, Karnataka, India

Job Type: Full time

Category: AI Engineering / Customer Success Operations

Date Posted: 2026-09-02

Experience Level: 5-10 years

Remote Status: Hybrid

🚀 Role Summary

• Drive customer success initiatives through the strategic implementation and development of AI-powered solutions within the Salesforce ecosystem.

• Contribute to the advancement of Salesforce's "Agentforce" initiative, focusing on shaping the future of AI in customer engagement and workforce transformation.

• Leverage AI engineering expertise to innovate and redefine how businesses leverage AI for enhanced customer experiences and operational efficiency.

• Collaborate with cross-functional teams to integrate cutting-edge AI technologies into CRM platforms, ensuring alignment with Salesforce's core values and business objectives.

• Focus on delivering measurable impact and exceptional customer outcomes by applying advanced AI engineering principles to real-world business challenges.

📝 Enhancement Note: While the job title suggests "UI LMTS - AI Engineering," the description and AI-derived keywords heavily emphasize AI Engineering, Customer Success, and the "Agentforce" initiative. The role is positioned within Customer Success, indicating a strong focus on applying AI engineering principles to drive customer outcomes rather than purely front-end UI development. The "LMTS" acronym is not explicitly defined but likely relates to a specific internal Salesforce program or team designation.

📈 Primary Responsibilities

• Develop, implement, and optimize AI models and algorithms to enhance customer success processes and deliver personalized customer experiences.

• Design and build scalable AI solutions that integrate seamlessly with Salesforce's CRM platform and other customer-facing applications.

• Collaborate with product managers, engineers, and customer success teams to translate business requirements into robust AI engineering solutions.

• Conduct research and stay abreast of the latest advancements in AI, machine learning, and agentic AI to identify opportunities for innovation.

• Analyze performance metrics of AI-driven solutions, identify areas for improvement, and implement data-driven optimizations to maximize impact.

• Contribute to the development and refinement of the "Agentforce" platform, ensuring its effectiveness in transforming workforce capabilities through AI.

• Troubleshoot and resolve technical issues related to AI systems and integrations within the Salesforce environment.

• Document AI engineering processes, methodologies, and solutions for knowledge sharing and future reference.

• Champion Salesforce's core values, particularly innovation, trust, and customer success, in all aspects of work.

📝 Enhancement Note: The responsibilities are inferred based on the job title "AI Engineering" and the context provided by Salesforce's emphasis on AI, CRM, and "Agentforce." The core focus is on applying AI engineering to enhance customer success operations and contribute to strategic initiatives like Agentforce.

🎓 Skills & Qualifications

Education:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related quantitative field.

• Advanced degrees or specialized certifications in AI/ML are highly desirable. Experience:

• 5-10 years of progressive experience in AI engineering, machine learning, or a closely related field.

• Proven track record of successfully developing and deploying AI/ML models in production environments.

• Experience within the CRM or SaaS industry is a significant advantage. Required Skills:

• Strong proficiency in AI engineering principles, including model development, training, evaluation, and deployment.

• Expertise in programming languages commonly used in AI/ML, such as Python, R, or Java.

• Familiarity with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).

• Deep understanding of CRM systems, particularly Salesforce, and their integration with AI solutions.

• Experience with data science methodologies, statistical analysis, and data manipulation techniques.

• Excellent problem-solving skills and the ability to tackle complex technical challenges.

• Strong communication and collaboration skills, with the ability to articulate technical concepts to diverse audiences. Preferred Skills:

• Experience with natural language processing (NLP), deep learning, or reinforcement learning.

• Knowledge of "Agentforce" or similar AI-driven agent technologies.

• Familiarity with cloud platforms (AWS, Azure, GCP) for AI/ML workloads.

• Experience with data visualization tools for presenting AI model performance.

• Understanding of software development lifecycle (SDLC) and agile methodologies.

• Contributions to open-source AI/ML projects or a strong GitHub profile.

📝 Enhancement Note: The experience level (5-10 years) is derived from the ai_experience_level field. The skills are extrapolated from the job title "AI Engineering" and Salesforce's general technology stack and focus on AI and CRM.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable examples of AI/ML models developed and deployed, showcasing impact and ROI.

• Case studies detailing the process of problem identification, data collection, model selection, development, and validation.

• Documentation of system architecture and integration strategies for AI solutions within complex environments like CRM.

• Evidence of contributions to process optimization through AI, highlighting efficiency gains or improved customer outcomes.

• Examples of code repositories (e.g., GitHub) showcasing AI engineering projects and best practices. Process Documentation:

• Detailed explanations of the AI model development lifecycle, from experimentation to production deployment.

• Workflow diagrams illustrating how AI solutions integrate into broader business processes, such as customer support or sales operations.

• Metrics and reporting frameworks used to track the performance, accuracy, and business impact of deployed AI models.

• Methodologies for AI model retraining, monitoring, and maintenance to ensure continued effectiveness and relevance.

📝 Enhancement Note: This section is crafted to align with typical expectations for AI engineering roles, emphasizing practical application, process rigor, and demonstrable impact, particularly within a CRM context.

💵 Compensation & Benefits

Salary Range:

• Estimated Range for Bengaluru, India: ₹1,500,000 - ₹3,500,000 per annum (INR)

• Explanation: This estimate is based on industry benchmarks for AI Engineering roles with 5-10 years of experience in major Indian tech hubs like Bengaluru. Factors influencing this range include specific skill sets, the complexity of AI projects, and the overall demand for AI talent in the region. Salesforce, as a leading tech company, typically offers competitive compensation packages. This range is a guideline and may vary based on individual qualifications and the final negotiation.

Benefits:

• Comprehensive health insurance (medical, dental, vision) for employees and dependents.

• Generous paid time off (PTO), including vacation, sick leave, and public holidays.

• Retirement savings plans (e.g., Provident Fund) with company matching.

• Professional development opportunities, including access to training, certifications, and conferences.

• Employee Stock Purchase Program (ESPP) and/or stock options.

• Wellness programs and resources, including mental health support.

• Flexible work arrangements and hybrid work model support.

• Access to Salesforce's renowned internal learning platforms and resources. Working Hours:

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

• While core hours may apply, Salesforce generally offers flexibility to accommodate hybrid work arrangements and individual needs, aligning with project deadlines and team collaboration requirements.

📝 Enhancement Note: Salary range is a research-based estimate for Bengaluru, India, for an AI Engineer with 5-10 years of experience. Benefits and working hours are typical for large multinational tech companies like Salesforce and are tailored to align with the hybrid work arrangement.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software & Cloud Computing (CRM, AI, Customer Success)

Company Size: Over 70,000 employees globally. This large size offers stability, extensive resources, and diverse career opportunities, but also means operations roles may be highly specialized.

Founded: 1999. Salesforce has a long-standing history of innovation and market leadership, particularly in CRM, which influences its culture towards forward-thinking and customer-centric approaches.

Team Structure:

• The role is within the "Customer Success" job category, focusing on AI Engineering. This suggests a team that bridges technical AI expertise with a deep understanding of customer needs and business outcomes.

• The team likely consists of AI Engineers, Data Scientists, Customer Success Managers, and potentially Product Managers, fostering a collaborative environment.

• Reporting structure is within the Customer Success organization, indicating a direct line of sight to customer impact and business value generation. Methodology:

• Data-Driven Innovation: Salesforce emphasizes using data to drive innovation, product development, and customer strategies. Expect a culture that values data analysis and insights.

• Customer-Centricity: The "Customer Success" focus means a strong emphasis on understanding and exceeding customer expectations through technology and service.

• Agile Development: Given the tech industry and Salesforce's approach, agile methodologies are likely employed for project management and software development.

Company Website: https://www.salesforce.com/

📝 Enhancement Note: Company context is derived from the provided LinkedIn data and Salesforce's public profile, focusing on how these aspects influence an AI Engineering role within Customer Success.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as an experienced AI Engineer within the Customer Success domain, typically falling into a mid-to-senior individual contributor level. It requires significant technical expertise and the ability to drive strategic initiatives. The "UI LMTS" designation might indicate a specific program or advanced track within Salesforce.

Reporting Structure: The role reports into the Customer Success organization, likely to an Engineering Manager or Director focused on AI solutions for customer enablement. Collaboration will be extensive with customer success managers, product teams, and other engineering groups.

Operations Impact: This role directly impacts customer retention, satisfaction, and adoption of Salesforce products by leveraging AI. The success of "Agentforce" and other AI initiatives will be a key measure of impact, influencing how clients derive value from Salesforce's AI capabilities.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI/ML, specific algorithms, or emerging AI fields like generative AI or agentic AI.

• Leadership Track: Transition into a technical lead, architect, or management role within AI Engineering or Customer Success.

• Cross-Functional Mobility: Move into product management, data science leadership, or other engineering domains within Salesforce.

• Strategic Influence: Contribute to the strategic direction of AI adoption and "Agentforce" development at Salesforce.

• Industry Recognition: Potential to contribute to industry thought leadership through speaking engagements or publications.

📝 Enhancement Note: Career path analysis is based on the "AI Engineering" title, the "5-10 years" experience level, and Salesforce's reputation as a large tech company with established career progression frameworks.

🌐 Work Environment

Office Type: Salesforce operates with a hybrid work model. This role is based in Bengaluru, India, and will likely involve a combination of remote work and in-office collaboration. The office environment is designed to foster innovation and teamwork.

Office Location(s): Bengaluru, India. This is a major technology hub, offering a vibrant ecosystem of tech talent and resources.

Workspace Context:

• Collaborative Hubs: Office spaces are typically designed with collaborative zones, meeting rooms, and open areas to encourage interaction and brainstorming.

• Technology-Enabled: Expect access to modern workstations, high-speed internet, and the necessary software and tools for AI development and collaboration.

• Team Interaction: Regular team meetings, stand-ups, and cross-functional project discussions will occur both virtually and in person.

Work Schedule: The standard 40-hour work week is expected, with flexibility often provided within the hybrid model. Employees are encouraged to manage their time effectively to meet project demands while maintaining work-life balance.

📝 Enhancement Note: This section infers the work environment based on Salesforce's stated hybrid work policy and the typical office setup of major tech companies in India.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or recruiter review of application and resume, likely with AI assistance, followed by a brief call to assess basic qualifications and interest.

• Technical Screening: A technical interview, potentially involving coding challenges or system design questions focused on AI/ML concepts and practical application.

• Hiring Manager Interview: Discussion on experience, motivations, alignment with Salesforce values, and deeper dive into relevant projects.

• Team/Peer Interviews: Interviews with potential colleagues to assess technical depth, problem-solving approach, and cultural fit. This may include a portfolio review.

• Final Round: Possibly with senior leadership or a panel to discuss strategic thinking and overall fit.

Portfolio Review Tips:

• Showcase AI/ML Lifecycle: Clearly present projects that demonstrate your end-to-end AI/ML capabilities, from problem definition and data handling to model deployment and impact measurement.

• Quantify Impact: Use specific metrics (e.g., accuracy improvements, efficiency gains, cost savings, revenue impact) to highlight the value of your AI solutions.

• Technical Depth: Be prepared to discuss the technical choices made in your projects – algorithm selection, framework usage, architectural decisions, and trade-offs.

• Relevance to Salesforce: Tailor your portfolio to highlight experience with CRM, customer success, or similar enterprise software challenges if possible.

• Code Samples: Provide links to well-documented code repositories (e.g., GitHub) for relevant projects.

Challenge Preparation:

• AI/ML Problem Solving: Be ready to tackle hypothetical AI/ML problems related to customer success, CRM data, or agentic AI. Practice breaking down problems, proposing solutions, and discussing potential challenges.

• System Design: Prepare for questions on designing scalable AI systems, considering data pipelines, model serving, and integration with existing platforms.

• Salesforce Ecosystem: Familiarize yourself with Salesforce's platform, its core functionalities, and how AI can be applied within it.

📝 Enhancement Note: The interview and portfolio review process is structured based on industry best practices for AI Engineering roles at large tech companies, with specific emphasis on demonstrating AI lifecycle management and quantifiable impact.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary), R, Java, Scala.

• AI/ML Frameworks: TensorFlow, PyTorch, scikit-learn, Keras.

• Data Processing: Apache Spark, Pandas, NumPy.

• Cloud Platforms: AWS (SageMaker, EC2, S3), Azure ML, Google Cloud AI Platform.

• Version Control: Git, GitHub/GitLab/Bitbucket.

Analytics & Reporting:

• Data Warehousing/Lakes: Snowflake, Redshift, BigQuery.

• BI Tools: Tableau, Power BI, Looker (or Salesforce's internal equivalents).

• Notebooks: Jupyter Notebooks, Google Colab.

CRM & Automation:

• CRM: Salesforce (core platform knowledge is essential).

• Orchestration/Workflow: Tools for managing AI pipelines and workflows, potentially Airflow, Kubeflow, or Salesforce's own automation tools.

• Containerization: Docker, Kubernetes (for deployment and scaling).

📝 Enhancement Note: This technology stack is representative of what an AI Engineer at a company like Salesforce would likely encounter, focusing on core AI/ML tools, data processing, cloud infrastructure, and CRM integration.

👥 Team Culture & Values

Operations Values:

• Innovation: Continuously seeking new ways to leverage AI to solve problems and create value for customers.

• Customer Success: A deep commitment to ensuring customers achieve their desired outcomes with Salesforce products.

• Trust & Integrity: Upholding the highest standards in data handling, AI model development, and business practices.

• Collaboration: Working effectively across teams to achieve shared goals and deliver integrated solutions.

• Equality: Promoting a diverse and inclusive workplace where all employees are valued and respected.

Collaboration Style:

• Cross-Functional Integration: Strong emphasis on working closely with Customer Success Managers, Product Managers, and other engineering teams to ensure AI solutions meet business needs.

• Data-Driven Decision Making: Collaborative discussions are informed by data analysis and performance metrics.

• Knowledge Sharing: A culture that encourages sharing best practices, learnings, and insights through internal forums, presentations, and documentation.

📝 Enhancement Note: Values and collaboration style are inferred from Salesforce's publicly stated core values and the nature of an AI Engineering role within a Customer Success organization.

⚡ Challenges & Growth Opportunities

Challenges:

• Scalability of AI Solutions: Ensuring AI models and systems can handle the massive scale of Salesforce customer data and user interactions.

• Ethical AI & Bias Mitigation: Developing and deploying AI responsibly, addressing potential biases in data and algorithms to ensure fairness and equity.

• Integration Complexity: Seamlessly integrating advanced AI capabilities into the existing, complex Salesforce ecosystem and diverse customer environments.

• Rapidly Evolving AI Landscape: Keeping pace with the constant advancements in AI research and technology to maintain a competitive edge.

Learning & Development Opportunities:

• Advanced AI/ML Training: Access to internal and external courses, workshops, and certifications in specialized AI fields.

• Industry Conferences: Opportunities to attend and present at leading AI and tech conferences (e.g., NeurIPS, ICML, Dreamforce).

• Mentorship Programs: Participation in mentorship programs to gain guidance from senior AI leaders and engineers.

• Exposure to Cutting-Edge Projects: Working on high-impact, innovative projects like "Agentforce" that push the boundaries of AI application.

📝 Enhancement Note: Challenges and growth opportunities are typical for senior AI engineering roles in a large, innovative tech company, with specific emphasis on AI ethics and the evolving nature of the field.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex AI/ML project you led. What was the business problem, your approach, the challenges you faced, and the quantifiable outcome?" (Focus on demonstrating problem-solving, technical execution, and impact measurement.)

• "How would you design an AI system to personalize customer journeys within a CRM like Salesforce, considering data privacy and scalability?" (Prepare to discuss system architecture, data pipelines, model types, and integration strategies.)

• "Salesforce is investing heavily in 'Agentforce.' How do you see AI engineering contributing to the success of agentic AI in customer success?" (Research 'Agentforce' and be ready to articulate your vision and technical contributions.) Company & Culture Questions:

• "Why are you interested in working at Salesforce, and specifically in this AI Engineering role within Customer Success?" (Align your answer with Salesforce's mission, values, and the role's impact.)

• "How do you stay updated with the latest advancements in AI and machine learning?" (Showcase your commitment to continuous learning and your preferred learning resources.)

• "Describe a time you had to collaborate with a non-technical team. How did you ensure clear communication and alignment?" (Highlight your communication and cross-functional collaboration skills.) Portfolio Presentation Strategy:

• Storytelling: Frame your portfolio projects as compelling stories, highlighting the "before" (the problem), the "during" (your solution and process), and the "after" (the achieved results and impact).

• Visual Aids: Use clear diagrams, charts, and visualizations to explain complex technical concepts and data findings.

• Focus on ROI: Emphasize the business value and return on investment your AI solutions delivered. Quantify everything possible.

• Technical Depth & Trade-offs: Be prepared to discuss the technical details, including why certain algorithms or approaches were chosen and what alternatives were considered.

📝 Enhancement Note: Interview preparation advice is tailored to an AI Engineering role at Salesforce, incorporating company-specific initiatives like "Agentforce" and general expectations for technical interviews.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Salesforce Careers portal via the provided link.

• Tailor Your Resume: Highlight specific AI engineering experience, proficiency in Python/TensorFlow/PyTorch, CRM knowledge (especially Salesforce), and any experience with customer success or agentic AI initiatives. Use keywords from the job description.

• Prepare Your Portfolio: Curate 2-3 key AI/ML projects that showcase your end-to-end development process, quantifiable impact, and technical expertise. Be ready to present these in detail.

• Research Salesforce & Agentforce: Understand Salesforce's mission, core values, and their strategic focus on AI and "Agentforce." Prepare to discuss how your skills align with their vision.

• Practice Technical & Behavioral Questions: Rehearse answers to common AI/ML technical questions and behavioral questions, focusing on STAR method (Situation, Task, Action, Result) for past experiences.

⚠️ 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.

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