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