AI Product Engineer - Agentic AI Platforms (Financial Services)
About the role
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.
Responsibilities
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.
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).
Integrate GenAI agents with client systems via APIs, workflow engines, event streams, and data platforms.
Implement CI/CD pipelines for agent code, prompts, and policies.
Implement 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.
Qualifications
Deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC.
Experience working directly with Financial Services clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments.
Familiarity with core insurance platforms like Guidewire, DuckCreek or Majesco.
Knowledge of P&C Insurance is a significant plus.
Strong understanding of Python and other relevant programming languages.
Experience with LLM providers such as Anthropic (Claude), OpenAI, and open-source models.
Experience with vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate).
Experience with DSPy and declarative prompt optimization techniques.
Experience with LangChain and LangGraph for agent orchestration, workflows, and control flow.
Experience with automated testing of agent flows and guardrails.
Experience with CI/CD pipelines for agent code, prompts, and policies.
Experience with observability, including tracing, decision logging, tool usage, and failure analysis.
Experience with evaluation frameworks for hallucination detection, consistency checks, and fitness scoring.
Experience with designing feedback loops incorporating human-in-the-loop review and reinforcement.
Experience with monitoring cost, latency, throughput, and behavioral drift across deployed agents.
Experience with designing 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.
Experience with producing documentation supporting risk, compliance, internal audit, and regulator engagement.
Experience with providing technical leadership and mentorship to consulting delivery teams.
Experience with contributing to internal GenAI accelerators, agent frameworks, and reusable assets.
Experience with supporting RFPs, proposals, and client solution designs with credible GenAI and agentic architectures.
Experience with participating in thought leadership on Agentic SDLC, GenAI engineering, and responsible autonomy.
Skills
Strong problem-solving skills and ability to work independently and collaboratively.
Excellent communication and presentation skills, both written and verbal.
Ability to translate complex technical concepts into clear, actionable solutions for non-technical stakeholders.
Experience with Agile methodologies and iterative development processes.
Experience with DevOps practices and tools.
Experience with cloud platforms and infrastructure as code.
Experience with data management and analytics.
Experience with machine learning and natural language processing.
Experience with software development best practices and standards.
Experience with project management tools and methodologies.
Experience with financial services regulations and compliance requirements.
Benefits
Competitive salary and performance-based bonuses
Comprehensive benefits package
Career development and training opportunities
Flexible work arrangements (remote and/or office-based)
Dynamic and inclusive work culture within a globally known group
Private Health Insurance
Retail Benefits
Paid Time Off
Training & Development
Pay
Competitive salary and performance-based bonuses
Schedule
Flexible work arrangements (remote and/or office-based)