Generative AI Engineering Lead
Citi · Pune, India +1 · 10d ago
Job Overview
We are seeking a results-driven Generative AI practitioner with end-to-end experience for the execution and deployment of cutting-edge Generative AI and agentic AI solutions across our enterprise-wide Controls Technology platform. In this role, you will be responsible for translating AI strategy into tangible, production-ready capabilities that enhance operational efficiencies and drive business value. We're looking for someone who combines deep technical expertise in generative AI — including context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration — with a proven track record of successfully delivering complex technology projects. This role centers on architecting and delivering solutions built on pre-trained and hosted foundation models, not on training or fine-tuning models.
Key Responsibilities
GenAI Delivery Leadership: Execute the delivery roadmap for generative and agentic AI projects, ensuring alignment with business objectives and timelines. Manage the project lifecycle from ideation and scoping to deployment and post-launch support.
Team Leadership & Mentorship: Build, mentor, and manage a high-performing team of AI engineers and specialists. Foster a culture of execution, collaboration, and continuous improvement to successfully deliver on the AI roadmap.
End-to-End Solution Delivery: Oversee the design, development, and deployment of robust, scalable, and production-ready GenAI and agentic applications. Ensure all solutions meet rigorous performance, security, and quality standards before and after deployment.
Agentic Solution Delivery: Drive the design and delivery of agentic workflows and multi-agent systems, establishing standards for agent harnesses, orchestration patterns, and reliable long-running agent execution across the platform.
Stakeholder & Program Management: Serve as the primary point of contact for GenAI delivery. Manage stakeholder expectations, communicate project progress, identify and mitigate risks, and ensure on-time and on-budget delivery.
Cross-Functional Partnership: Collaborate closely with Data Mesh, Cloud Architecture, MLOps/LLMOps, and business unit teams to ensure the seamless integration and operationalization of GenAI and agentic solutions into our existing technology ecosystem.
Technical Excellence & Best Practices: Drive the adoption of best practices in software development (CI/CD), LLMOps, agent observability, and project management (Agile/Scrum) within the AI team to ensure efficient and repeatable delivery.
Governance & Ethical Deployment: Implement and enforce robust governance and ethical AI frameworks throughout the delivery process — including guardrails, agent isolation/sandboxing, and responsible AI practices — ensuring compliance with data privacy standards and corporate policies.
Required Technical Skills
Core Generative AI Concepts: Deep understanding of foundation models, LLMs, embeddings, tokenization, and context-window management. Fluent in applying pre-trained and hosted models to enterprise use cases.
Context Engineering: Expertise in advanced context engineering — context layering, chaining, compression, pruning/offloading, and memory management — to maximize reliability, provenance, and token efficiency in production.
Prompt Engineering: Adept at advanced prompt engineering techniques and best practices, with familiarity with frameworks that facilitate effective prompt design and management.
Retrieval-Augmented Generation (RAG): Advanced knowledge of RAG techniques, including hybrid search, multi-vector retrieval, Hypothetical Document Embeddings (HyDE), self-querying, query expansion, re-ranking, and relevance filtering.
Knowledge Graphs & Graph RAG: Experience designing and delivering knowledge graphs (e.g., using graph databases such as Neo4j or ArangoDB) and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.
Agentic AI & Multi-Agent Orchestration: Proven experience delivering agentic systems using Google Agent Development Kit (ADK) and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.
Agent Harness & Interoperability: Strong grasp of harness engineering (governance, constraints, feedback loops, execution controls, agent isolation/sandboxing) and agent interoperability protocols — the Model Context Protocol (MCP) for tool/data access and the Agent2Agent (A2A) protocol for inter-agent collaboration.
Machine Learning Frameworks & Cloud Computing: Working knowledge of ML frameworks and extensive hands-on experience with AWS (or equivalent) services and infrastructure for AI/GenAI.
Natural Language Processing (NLP) & AI Deployment: Advanced NLP skills (NER, dependency parsing, text classification, topic modeling). Expertise in containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for LLMOps.
Data Engineering & API Development: Strong proficiency in data preprocessing, document ingestion, and handling large-scale datasets. Experience with real-time and streaming AI applications and designing RESTful APIs for model and agent integration.
Generative AI Tools & Platforms: Experienced with LangGraph, Autogen, CrewAI, LangChain, LlamaIndex, Hugging Face, and Google ADK. Familiarity with major GenAI APIs (OpenAI, Gemini, Claude) and version control systems like Git.
Agent Observability & Evaluation: Experience with tracing and evaluation tooling (e.g., OpenTelemetry-based observability) for production GenAI and agent systems.
AI Compliance & Guardrails: Knowledge of AI compliance frameworks and best practices. Experience implementing guardrails to ensure ethical AI usage and mitigate risks (e.g., Microsoft's AI Guidance Framework).
Required Leadership & Soft Skills
Delivery Leadership: Proven ability to lead and deliver complex, large-scale technical projects from concept to production.
Program Management: Expertise in Agile/Scrum methodologies, project planning, resource allocation, and risk management.
Strategic Execution: Capacity to translate high-level AI strategy into a concrete, actionable delivery plan and execute it effectively.
Stakeholder Management: Exceptional ability to manage expectations, communicate complex technical topics clearly, and build strong relationships with both technical and non-technical stakeholders.
Pragmatic Innovation: A passion for applying cutting-edge GenAI and agentic technologies to solve real-world business problems in a practical and efficient manner.
Problem Solving: Proactive and analytical mindset to overcome technical and logistical challenges in a fast-paced environment.
Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field (PhD preferred).
Total Experience of 14+ years. 8+ years of experience in AI/ML, with at least 3 years in Generative AI (including agentic AI).
5+ years of leadership experience managing technical teams and delivering complex software or AI solutions.
Extensive hands-on experience with AWS services and infrastructure related to AI/GenAI.
A strong portfolio of projects showcasing the successful delivery of AI solutions into a production business environment.
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Job Family Group:
Technology------------------------------------------------------
Job Family:
Applications Development------------------------------------------------------
Time Type:
Full time------------------------------------------------------
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