AI Product Engineer - Agentic AI Platforms (Financial Services)

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.?

• 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.?

• 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, event streams, and data platforms.?

• Implement?agent observability?including tracing, decision logging, tool usage, and failure analysis.?

• Apply?evaluation frameworks?for hallucination detection, consistency checks, and fitness scoring.?

• Design feedback loops incorporating?human?in?the?loop review and reinforcement.?

• Monitor cost, latency, throughput, and behavioral drift across deployed agents.?

• Design Agentic AI platforms aligned with?Financial Services regulatory expectations, including:?

• Auditability and traceability of agent decisions?

• Model and prompt explainability?

• Data privacy and security controls?

• Resilience and fail?safe mechanisms?

• Embed?guardrails and policies?addressing hallucination risk, bias, unauthorized actions, and escalation failures.?

• Produce documentation supporting?risk, compliance, internal audit, and regulator engagement.?

• Provide technical leadership and mentorship to consulting delivery teams.?

• Contribute to internal?GenAI accelerators, agent frameworks, and reusable assets.?

• Support?RFPs, proposals, and client solution designs?with credible GenAI and agentic architectures.?

• Participate in thought leadership on?Agentic SDLC, GenAI engineering, and responsible autonomy.?

About Capgemini Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of over 420,000 team members in more than 50 countries. With its strong 55-year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group ?22.5 billion in revenues in 2025.