{"id":971403,"url":"https://alion.io/job/citi-generative-ai-engineering-lead-2","title":"Generative AI Engineering Lead","company":{"id":7841,"name":"Citi","domain":"citi.com","url":"https://alion.io/company/citi","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":98,"open_postings":557,"ghost_share":0.009,"stale_share":0.206,"repost_share":0.068,"time_to_fill_p50_days":9,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India","Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":61000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":14,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"ArangoDB","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"Google ADK","optional":false},{"name":"GraphRAG","optional":false},{"name":"Hugging Face","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Neo4j","optional":false},{"name":"NER","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Scrum","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-09-16T18:01:32Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-11-08T15:26:13Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":53},"description":"Job Overview\nWe 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.\nKey Responsibilities\nGenAI 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.\n\nTeam 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.\n\nEnd-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.\n\nAgentic 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.\n\nStakeholder & 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.\n\nCross-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.\n\nTechnical 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.\n\nGovernance & 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.\n\nRequired Technical Skills\nCore 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.\n\nContext Engineering: Expertise in advanced context engineering - context layering, chaining, compression, pruning/offloading, and memory management - to maximize reliability, provenance, and token efficiency in production.\n\nPrompt Engineering: Adept at advanced prompt engineering techniques and best practices, with familiarity with frameworks that facilitate effective prompt design and management.\n\nRetrieval-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.\n\nKnowledge 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.\n\nAgentic 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.\n\nAgent 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.\n\nMachine Learning Frameworks & Cloud Computing: Working knowledge of ML frameworks and extensive hands-on experience with AWS (or equivalent) services and infrastructure for AI/GenAI.\n\nNatural 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.\n\nData 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.\n\nGenerative 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.\n\nAgent Observability & Evaluation: Experience with tracing and evaluation tooling (e.g., OpenTelemetry-based observability) for production GenAI and agent systems.\n\nAI 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).\n\nRequired Leadership & Soft Skills\nDelivery Leadership: Proven ability to lead and deliver complex, large-scale technical projects from concept to production.\n\nProgram Management: Expertise in Agile/Scrum methodologies, project planning, resource allocation, and risk management.\n\nStrategic Execution: Capacity to translate high-level AI strategy into a concrete, actionable delivery plan and execute it effectively.\n\nStakeholder Management: Exceptional ability to manage expectations, communicate complex technical topics clearly, and build strong relationships with both technical and non-technical stakeholders.\n\nPragmatic Innovation: A passion for applying cutting-edge GenAI and agentic technologies to solve real-world business problems in a practical and efficient manner.\n\nProblem Solving: Proactive and analytical mindset to overcome technical and logistical challenges in a fast-paced environment.\n\n Qualifications\nBachelor's or Master's degree in Computer Science, Data Science, AI, or a related field (PhD preferred).\n\nTotal Experience of 14+ years. 8+ years of experience in AI/ML, with at least 3 years in Generative AI (including agentic AI).\n\n5+ years of leadership experience managing technical teams and delivering complex software or AI solutions.\n\nExtensive hands-on experience with AWS services and infrastructure related to AI/GenAI.\n\nA strong portfolio of projects showcasing the successful delivery of AI solutions into a production business environment.\n\n------------------------------------------------------\nJob Family Group:\nTechnology------------------------------------------------------\nJob Family:\nApplications Development------------------------------------------------------\nTime Type:\nFull time------------------------------------------------------\nMost Relevant Skills\nPlease see the requirements listed above.------------------------------------------------------\nOther Relevant Skills\nFor complementary skills, please see above and/or contact the recruiter.------------------------------------------------------\nCiti is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.\nIf you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.\nView Citi’s EEO Policy Statement and the Know Your Rights 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