Agentic AI Engineer - GCP
About The Position
Capgemini is seeking an Agentic AI Engineer to design, develop, and deploy intelligent AI agents using the Google Cloud Platform (GCP) AI ecosystem. The role uses technologies including Vertex AI, Gemini models, Agent Builder, Vector Search, BigQuery, and other Google Cloud services to build autonomous, goal-driven AI solutions that automate business processes, enhance customer experiences, and improve operational efficiency. It calls for expertise in Generative AI, Agentic AI architectures, Retrieval-Augmented Generation (RAG), orchestration frameworks, enterprise-scale AI deployment, and automation testing. The engineer will lead the design and delivery of enterprise-grade Generative AI and Agentic AI solutions, build scalable RAG and multi-agent systems, integrate large language models with enterprise applications and tools, and deploy cloud-native AI applications. The position also provides technical leadership through architecture reviews, proofs of concept, solution design, troubleshooting, and delivery governance, while collaborating with stakeholders and mentoring engineering teams. The position may be remote or based in Nashville, Tennessee.
Requirements
- Strong hands-on experience with GCP, particularly Vertex AI, for building and deploying enterprise-scale Generative AI solutions.
- Proven expertise designing and implementing RAG architectures, including document ingestion, retrieval pipelines, and contextual response generation.
- Deep understanding of embeddings, vector databases, semantic search, and grounding techniques.
- Extensive experience developing Agentic AI and multi-agent systems using frameworks such as LangChain and related orchestration technologies.
- Advanced Python programming skills and enterprise application development experience using Java, C#, or Node.js.
- Strong knowledge of SQL and NoSQL databases, including data modeling, optimization, and integration with AI-driven applications.
- Experience with automation testing.
- Experience building cloud-native and containerized applications using Docker, Kubernetes, microservices, and serverless architectures.
- Background in DevOps, CI/CD, MLOps, and AI model lifecycle management.
- Technical leadership, stakeholder management, problem-solving, and communication skills, with a track record of leading teams and delivering AI transformation initiatives.
Responsibilities
- Lead the design, development, and deployment of enterprise-grade Generative AI and Agentic AI solutions on GCP using Vertex AI.
- Architect and implement scalable RAG solutions, including data ingestion, embeddings, vector search, and retrieval pipelines.
- Develop and optimize grounding and embedding strategies to improve response accuracy, relevance, and factual consistency.
- Build and manage Agentic AI and multi-agent systems for autonomous decision-making, orchestration, and task execution.
- Integrate LLMs with enterprise applications, APIs, and external tools using structured interaction frameworks such as Model Context Protocol (MCP).
- Design and deploy cloud-native AI applications using Docker, Kubernetes, microservices, serverless technologies, and modern MLOps/CI-CD practices.
- Perform automation testing.
- Provide technical leadership through architecture reviews, proofs of concept, solution design, troubleshooting, and delivery governance.
- Collaborate with cross-functional stakeholders and mentor engineering teams to drive AI innovation, best practices, and business outcomes.
Benefits
- Paid time off, including 12–25 vacation days depending on employee grade, company-paid holidays, personal days, and sick leave.
- Medical, dental, and vision coverage (or provincial healthcare coordination in Canada).
- Retirement savings plans, such as a 401(k) in the U.S. or RRSP in Canada.
- Life and disability insurance.
- Employee assistance programs.
- Additional benefits as provided by local policy and eligibility.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
