AI/ML Engineer - Agentic AI & Vertex AI
Listed on 2026-09-03
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Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer
AI/ML Engineer - Agentic AI & Vertex AI
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Location Atlanta, Chicago, Dallas, NJ, Nashville
At Capgemini, you will collaborate with cross-functional teams to deliver innovative technology solutions that drive business value and enhance client experiences. You will contribute to the design, development, and continuous improvement of scalable, high-quality solutions in a dynamic and collaborative environment.
Key Responsibilities1. Agentic AI Design & Implementation
Design and develop intelligent AI agents using Vertex AI Agent Builder to automate complex business processes and workflows. Leverage the Agent Development Kit (ADK) to build, orchestrate, and manage multi-agent systems capable of collaborating on end-to-end business challenges. Implement and integrate Model Context Protocol (MCP) Toolbox to securely connect AI agents with enterprise data platforms such as Big Query and Cloud Spanner.
Architect scalable agentic solutions that effectively combine reasoning, retrieval, tool usage, and workflow automation.
2. AI-Driven Data Strategy & Engineering
Utilize Vertex AI for model training, fine-tuning, evaluation, and deployment, while integrating seamlessly with Big Query for feature engineering and analytics. Build and optimize real-time and batch data pipelines using services such as Dataflow to support large-scale AI and ML workloads. Enable low-latency inference through Vertex AI Endpoints and Run Inference APIs for production-grade AI applications. Implement retrieval-augmented architectures using vector search capabilities within Big Query and AlloyDB, ensuring AI systems remain grounded in current business context and reducing knowledge drift.
Operational Expectations (Soft Skills)
Active Participation:
Attend internal and customer-facing meetings punctually and consistently. Remain actively engaged in technical discussions, reviews, and planning sessions.
Transparent Communication:
Provide regular, structured updates on project progress, milestones, risks, and technical blockers. Communicate effectively with both technical teams and business stakeholders.
Proactive
Collaboration:
Seek guidance when encountering challenges and contribute to a collaborative problem-solving culture. Support peers through knowledge sharing, code reviews, and troubleshooting efforts.
Consultative Mindset:
Work closely with stakeholders to translate business objectives into scalable and maintainable technical solutions. Navigate complex enterprise environments and align AI initiatives with organizational goals.
Vertex AI Expertise:
Strong hands-on experience with:
Vertex AI Model Garden, Vertex AI Pipelines, Model Evaluation and Optimization, Vertex AI Endpoints, Vertex AI Agent Builder.
Data & Machine Learning Engineering:
Advanced proficiency in SQL (Big Query) and Python for machine learning and data engineering. Experience with:
Data preprocessing and feature engineering, Data scaling and normalization, Encoding techniques, Missing value imputation, Model performance monitoring.
Cloud & Infrastructure:
Practical experience with:
Google Cloud Platform (GCP), Google Cloud Storage (GCS), Big Query, Cloud Spanner, Vertex AI Endpoints.
Emerging AI Technologies:
Understanding of modern agentic AI architectures and multi-agent systems. Familiarity with stateful real-time processing, contextual memory, retrieval systems, and AI orchestration frameworks. Knowledge of current trends and innovations in Generative AI and autonomous agents.
Experience in Financial Services, Banking, Fin Tech, or Retail domains. Understanding of industry-specific use cases such as:
Credit Risk Assessment, Fraud Detection, Royalty Forecasting, Search Relevance Optimization, Customer Intelligence…
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