Gen AI - Data Engineer with AWS Experience
Backend Developer
Agentic Ai
Ai Agent
Ai Agent Platform
Ai Platform
Ai Security
Artificial Intelligence
Artificial Intelligence Engineer
Cloud Operations
Data Analysis
Data Analytics
Data Architecture
Data Engineer
Data Integration
Data Integration Tools
Data Lakehouse
Data Pipeline
Data Platform
Data Processing
Databases
Databricks Lakehouse
Engineer
ETL
Generative AI
Informatica
Programming
Rag Architectures
Software Development
Vector Databases
Job Description
Capgemini is seeking a contract Data Engineer with AWS experience to architect, build, and scale AI-powered “Store AI Agents” that support store operations. This role blends hands-on engineering with technical leadership, taking GenAI solutions from architecture through production readiness using agentic workflows and RAG capabilities.
Key Responsibilities
- Own end-to-end architecture and implementation of Store AI Agents.
- Design agentic workflows that connect LLMs with APIs, databases, backend services, enterprise applications, and MCP servers.
- Build and optimize RAG solutions, including embeddings, retrieval, vector search, grounding, and context management.
- Define how new AI agents are onboarded and integrated, including testing, deployment, monitoring, and production readiness.
- Set the system architecture that separates LLM reasoning and generation from deterministic backend services.
- Apply Lakehouse/Medallion architecture (Bronze, Silver, Gold) to enable AI and data use cases.
- Create scalable approaches for AI/RAG testing, validation, observability, and performance optimization.
- Handle production engineering concerns such as latency, context limitations, retrieval quality, scalability, and reliability.
- Provide technical leadership across AI/ML, data engineering, cloud, application, and business teams.
Required Qualifications
- Demonstrated experience delivering at least one end-to-end LLM, RAG, or agentic AI project.
- Strong architectural and hands-on experience with Generative AI on AWS.
- Hands-on knowledge of LLMs, RAG, embeddings, vector search, and agentic workflows.
- Experience integrating AI agents with APIs, databases, backend services, and enterprise systems.
- Understanding of MCP (Model Context Protocol) and how agent-to-tool or service integration is implemented.
- Experience with Lakehouse and Medallion Architecture, plus data pipeline optimization.
- Strong grasp of AI testing, observability, security, scalability, latency, and overall production performance.
- Ability to clearly explain end-to-end architecture, technology choices, integration patterns, and design trade-offs.
Technologies
- AWS
- LLMs
- RAG
- Embeddings
- Vector search
- MCP (Model Context Protocol)
- Lakehouse
- Medallion Architecture
Location and Employment Details
- Location: Dallas, TX, United States (onsite)
- Job type: Contract
- Compensation: USD 37 - 59 per hour
Benefits
- Medical
- Dental
- Vision
- Retirement benefits
Preferred
- Experience with AWS-based GenAI and data platforms.
- Experience building enterprise-scale AI agents or multi-agent solutions.
- Exposure to modern vector databases, Lakehouse technologies, and AI orchestration frameworks.
- Strong technical leadership and stakeholder communication skills.