Senior Gen AI Engineer - Vice President

Citi · Chennai · 2+ yrs experience · Posted 2026-07-18

Tech stack: Angular, Java, Python, React, SQL

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About the role

We are looking for a Senior Gen AI Engineering leader to design and build AI/ML solutions that power intelligent digital experiences across customer-facing and operational platforms. This role requires deep expertise in AI/ML, including LLM-powered workflows, agentic AI systems, and machine learning models, to enable natural language understanding, intelligent automation, and personalized self-service at scale.
As a Vice President, you will lead a team with full responsibility for people, budget, and performance, while connecting intelligent AI systems with enterprise platforms across millions of multilingual customer interactions.
If you are passionate about building production-grade AI using LLMs, RAG, and Agentic AI and thrive on growing high-performing teams, this role is for you.
Responsibilities: - Generative AI & Agentic AI
- Lead the design and development of scalable, enterprise-grade conversational AI and agentic systems for real-time customer interaction use cases
- Build and optimize AI/ML solutions including LLM-powered workflows using GPT, Claude, Gemini, or equivalent models
- Design and deploy multi-agent systems to automate complex customer journeys and business workflows
- Apply advanced prompt engineering, evaluation frameworks, and guardrails to improve AI response quality, cost, and accuracy
- RAG & Knowledge Systems
- Develop and implement RAG pipelines and knowledge-based AI integrations
- Integrate vector databases and enterprise knowledge repositories
- Improve retrieval accuracy, response relevance, and grounding of AI-generated content Software Engineering
- Build scalable backend services and APIs using Java and/or Python.
- Develop cloud-native microservices and integrate AI capabilities into customer-facing applications.
- Collaborate with architects, product managers, and business stakeholders to deliver high-impact solutions.
- AI Operations & Governance
- Monitor model performance, latency, and operational costs.
- Implement AI governance, observability, and responsible AI practices.
- Optimize LLM usage and inference costs in production environments.
- Required Skills & Experience Must Have
- 11–16 years of experience in application development or enterprise engineering roles
- Expert-level proficiency in Python (mandatory)
- Strong hands-on expertise in Java, Microservices, and REST APIs
- 2+ years of hands-on experience with AI/ML and LLMs (e.g., GPT, Claude, Gemini)
Qualifications: - with RAG, prompt engineering, and vector databases with agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI)
- Strong understanding of system design, distributed systems, and cloud platforms
- Full-stack development experience with modern frontend frameworks (e.g., React.js / Angular)
- with SQL/NoSQL databases and real-time data processing
- Nice to Have
- MLOps experience (deployment, monitoring, model lifecycle management)
- with recommendation engines or conversational AI platforms of financial services, wealth management, or digital banking with AI governance and responsible AI practices
- Build AI-powered experiences at scale for millions of customers
- Deliver production-ready GenAI and Agentic AI solutions
- Drive customer engagement and operational efficiency through AI
- Lead high-performing teams to adopt and deliver emerging AI technologies
- Good to Have in Financial Services / Banking domain Exposure to recommendation systems, personalization, or conversational AI of customer journey analytics, sentiment analysis, or automation workflows working in product-based or large-scale enterprise environments Role Type
- Individual Contributor (Hands-on)
- High ownership role with cross-functional collaboration
- Strong technologist with the ability to bridge traditional engineering and AI
- Hands-on problem solver with a product mindset
- Ability to work in a fast-evolving AI landscape and drive innovation

Qualifications

- with RAG, prompt engineering, and vector databases with agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI)
- Strong understanding of system design, distributed systems, and cloud platforms
- Full-stack development experience with modern frontend frameworks (e.g., React.js / Angular)
- with SQL/NoSQL databases and real-time data processing
- Nice to Have
- MLOps experience (deployment, monitoring, model lifecycle management)
- with recommendation engines or conversational AI platforms of financial services, wealth management, or digital banking with AI governance and responsible AI practices
- Build AI-powered experiences at scale for millions of customers
- Deliver production-ready GenAI and Agentic AI solutions
- Drive customer engagement and operational efficiency through AI
- Lead high-performing teams to adopt and deliver emerging AI technologies
- Good to Have in Financial Services / Banking domain
- Exposure to recommendation systems, personalization, or conversational AI of customer journey analytics, sentiment analysis, or automation workflows working in product-based or large-scale enterprise environments Role Type
- Individual Contributor (Hands-on)
- High ownership role with cross-functional collaboration
- Strong technologist with the ability to bridge traditional engineering and AI
- Hands-on problem solver with a product mindset
- Ability to work in a fast-evolving AI landscape and drive innovation

Responsibilities

- Generative AI & Agentic AI
- Lead the design and development of scalable, enterprise-grade conversational AI and agentic systems for real-time customer interaction use cases
- Build and optimize AI/ML solutions including LLM-powered workflows using GPT, Claude, Gemini, or equivalent models
- Design and deploy multi-agent systems to automate complex customer journeys and business workflows
- Apply advanced prompt engineering, evaluation frameworks, and guardrails to improve AI response quality, cost, and accuracy
- RAG & Knowledge Systems
- Develop and implement RAG pipelines and knowledge-based AI integrations
- Integrate vector databases and enterprise knowledge repositories
- Improve retrieval accuracy, response relevance, and grounding of AI-generated content Software Engineering
- Build scalable backend services and APIs using Java and/or Python.
- Develop cloud-native microservices and integrate AI capabilities into customer-facing applications.
- Collaborate with architects, product managers, and business stakeholders to deliver high-impact solutions.
- AI Operations & Governance
- Monitor model performance, latency, and operational costs.
- Implement AI governance, observability, and responsible AI practices.
- Optimize LLM usage and inference costs in production environments.
- Required Skills & Experience Must Have
- 11–16 years of experience in application development or enterprise engineering roles
- Expert-level proficiency in Python (mandatory)
- Strong hands-on expertise in Java, Microservices, and REST APIs
- 2+ years of hands-on experience with AI/ML and LLMs (e.g., GPT, Claude, Gemini)

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