Senior Expert, IT Development - RAG & Agentic AI

About the role

Randstad Enterprise is looking for a Senior Expert with deep technical expertise to own the architecture, design and implementation of high-complexity RAG and Agentic AI solutions integrated with enterprise middleware and a microservices ecosystem. The role bridges AI research and scalable enterprise applications, ensuring technical standards and innovation align with business strategy.

Key responsibilities

• Lead the high-level design of AI agents and orchestration layers using frameworks such as LangChain or LlamaIndex.
• Direct the development and optimization of Retrieval-Augmented Generation pipelines, including embeddings, vector search and retrieval workflows.
• Integrate AI components into enterprise orchestration layers and business systems via secure RESTful APIs and microservices.
• Act as the technical escalation point, fixing complex errors and integration issues within the AI workload.
• Implement technical standards, methods and best practices for AI code quality, testing and documentation.
• Advise senior leadership on the feasibility of new AI products and influence the development roadmap.
• Set up frameworks for evaluating, benchmarking and monitoring models with standard metrics and observability tools.
• Lead project-based teams through the full development lifecycle of complex AI software or infrastructure.
• Partner with Product, DevOps, Security and Data teams, and identify quality and security risks in line with enterprise policies and OWASP standards.

Required profile

• 15+ years in software engineering with specialized expertise in high-complexity, large-scale distributed architectures.
• Master's degree in Computer Science, Software Engineering or a related field, or equivalent high-level experience.
• Preferred: production experience with orchestrated AI workflows or multi-agent systems, managed platforms such as GCP Vertex AI, AWS Bedrock or Azure OpenAI, MLOps and cost optimization, event-driven systems, and open-source AI/ML contributions.

Required skills

• Python (FastAPI, PyTorch, TensorFlow) or Java (Spring Boot).
• LLM orchestration (LangChain, LlamaIndex) and vector databases; RESTful API development.
• Data architecture, RDBMS and data pipelines for production model deployment.
• MLOps, Docker/Kubernetes, CI/CD, and cloud platforms (AWS/Azure/GCP).
• OAuth2, JWT and security protocols.