Job Opportunities API

Capgemini is at the forefront of Generative AI innovation, helping Financial Services clients industrialize GenAI and Agentic AI platforms at enterprise scale.  We are seeking an experienced and innovative AI Product Engineer – Agentic Platforms to join our Financial Services Artificial Intelligence & Business Lines (FS‑ABL) practice. This role is ideal for a consulting technologist with deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC, who can partner directly with clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments.  In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production‑grade, governed, multi‑agent systems. You will apply leading GenAI frameworks and LLM platforms — including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases—while operating across the full Agentic SDLC.  P&C Insurance knowledge and experience is a significant plus. Additionally, familiarity with core insurance platforms like Guidewire, DuckCreek or Majesco will be extremely helpful to succeed in this role. We are looking for candidates across all levels of experience and expertise - junior through senior level AI Product Engineers. Requirements Client Advisory & Product Vision  Partner directly with Financial Services clients to identify, prioritize, and shape Agentic AI use cases across customer operations, underwriting, claims, risk, compliance, finance, and technology.  Lead client workshops to define agent personas, responsibilities, autonomy boundaries, human‑in‑the‑loop checkpoints, and escalation logic.  Translate evolving business needs into agentic product backlogs, roadmaps, and MVP definitions.  Support executive conversations around GenAI platform strategy, operating models, vendor selection, and scale‑out approaches.  Agentic Platform & Architecture Design   Design and implement multi‑agent architectures using modern GenAI tooling, including:  Planner, executor, reviewer/critic, and supervisor agents  Tool‑calling and function‑calling agents  Memory‑enabled agents (conversation, semantic, episodic, and structured memory)  Leverage LangChain and LangGraph for agent orchestration, workflows, and control flow.  Apply DSPy and declarative prompt optimization techniques for repeatability, performance tuning, and regression control.  Design agent interaction patterns such as hierarchical agents, collaborating agents, and event‑driven agent workflows.  Define standardized agent contracts, interfaces, and schemas to enable reuse and scale.  Agentic SDLC & Engineering Delivery  Own delivery across the full Software Development Lifecycle (SDLC), extending it into a formal Agentic SDLC, including:  Agent design specifications and behavior contracts  Prompt, policy, and tool versioning  Simulation environments and offline evaluation  Automated testing of agent flows and guardrails  Controlled rollout, telemetry‑driven optimization, and continuous learning  Build production‑grade AI services primarily using Python, integrating:  LLM providers such as Anthropic (Claude), OpenAI, and open‑source models  Retrieval‑Augmented Generation (RAG) using vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate)  Implement CI/CD pipelines for agent code, prompts, and policies.  Integrate GenAI agents with client systems via APIs, workflow engines, e