Lead Agentic AI Engineer - FDE ( Forward Deploy Engineer)

Overview

Lead a Forward Deployed Engineer role at Citi, embedding with business stakeholders and operations teams to turn complex institutional workflows into enterprise-grade agentic AI solutions, from prototype through production deployment.

What you'll do

• Formulate and define systems scope and objectives for complex AI/GenAI projects, translating business requirements into technical specifications for Data Scientists and ML Engineers.
• Support end-to-end AI implementation and system change processes, from Business Requirement Documents (BRDs) to functional specifications, model deployment, testing, and integration with existing systems.
• Consult with business clients to determine functional specifications and develop custom scripts, integrations, or workflows to tailor AI/GenAI products to business needs.
• Solve complex system and model integration issues through in-depth evaluation of business processes, AI/ML systems, and industry standards.
• Act as a bridge between front-line business users and core AI research/engineering teams, gathering feedback on model performance and relaying insights to guide the AI product roadmap.
• Educate business and technical teams on leveraging deployed AI solutions, promoting best practices in prompt engineering, model interaction, and operational maintenance.
• Identify and communicate risks and impacts, including AI model limitations, data privacy, bias, and ethical considerations, and evaluate business implications of applying GenAI/ML technologies.
• Act as advisor or coach to new or lower-level analysts and work as a team to achieve business objectives.
• Rapidly convert ambiguous business problems into functional, production-ready Proof of Concepts (PoCs) in 2–4 week cycles, followed by hardened enterprise deployment.
• Sit alongside business heads, compliance officers, and operations leads to uncover automation bottlenecks and architect tailored AI agent interventions.
• Partner with Java/Python backend, UI, and DevOps engineers to integrate agent services into core transactional systems, client lifecycle management (CLM) platforms, and CI/CD pipelines.
• Lead the technical defense and documentation of agentic workflows through enterprise Model Risk Management (MRM), Responsible AI, and InfoSec approval gates.
• Assess risk when business decisions are made, driving compliance with applicable laws, rules and regulations, and escalating and reporting control issues with transparency.

What you'll need

• 7+ years of software engineering experience, with at least 3+ years dedicated to applied NLP, GenAI, LLM application development, and autonomous agent orchestration.
• Advanced proficiency in Python (FastAPI, Asyncio, Pydantic, LangChain/LangGraph) and strong working knowledge of Java / Spring Boot or modern TypeScript/Node environments.
• Proven experience building multi-agent systems, ReAct loops, Plan-and-Solve patterns, stateful workflows, memory management, and structured JSON output parsing.
• Hands-on experience with vector search engines (PostgreSQL with pgvector, Qdrant, Milvus, Pinecone) and relational/NoSQL datastores.
• Deep familiarity with leading foundational models (Claude, GPT-4, Gemini) and enterprise gateways (e.g., R2D2, AWS Bedrock, Azure OpenAI), including token optimization, rate-limiting, and cost modeling.
• Solid experience with containerization (Docker, Kubernetes/OpenShift), CI/CD pipelines, AWS/GCP cloud services (S3, IAM, Lambda/ECS), and enterprise observability (distributed tracing, OpenTelemetry).
• Ability to frame, scope, and deliver complex AI projects with minimal upfront requirements.
• Outstanding ability to articulate AI architectural trade-offs, confidence intervals, and ROI to technical and non-technical executive stakeholders.
• High-ownership mindset with the agility to shift from strategic architectural design to hands-on debugging in the same day.

Nice to have

• Prior experience in Institutional Banking, FinTech, Wealth Management, KYC/AML, or Fund Operations.
• Working knowledge of Model Context Protocol (MCP) or custom enterprise tool-calling frameworks.
• Experience with Model Risk Governance (MRM), Fair Lending, or Regulatory AI Compliance frameworks.
• Contributions to open-source agentic AI libraries, research publications, or community AI toolkits.

Details

• Location: Pune, Maharashtra, India.
• Employment type: Full time.
• Operates with limited level of direct supervision and independence of judgment.