AI Engineer - GenAI/Agentic Systems

A leading enterprise organization is expanding its next-generation AI engineering capabilities and seeking an experienced AI Engineer focused on GenAI, agentic systems, and advanced retrieval architectures. This role will help design, build, and scale enterprise-grade AI applications powered by LLMs, embeddings, knowledge graphs, and autonomous AI agents.

A leading enterprise organization is expanding its next-generation AI engineering capabilities and seeking an experienced AI Engineer focused on GenAI, agentic systems, and advanced retrieval architectures.

This role will help design, build, and scale enterprise-grade AI applications powered by LLMs, embeddings, knowledge graphs, and autonomous AI agents. The team is focused on developing highly scalable, production-ready AI systems that integrate across large enterprise data ecosystems.

RESPONSIBILITIES

Build and deploy GenAI applications using modern foundation models.

Develop advanced RAG and GraphRAG architectures.

Design autonomous AI agents using modern orchestration frameworks.

Create scalable APIs and AI services using Python and FastAPI.

Integrate enterprise data sources into LLM-powered applications.

Build evaluation and observability frameworks for GenAI systems.

Implement monitoring around hallucination rates, latency, cost, and groundedness.

Deploy AI services across AWS, Azure, or GCP environments.

Collaborate with engineering teams to evolve enterprise AI capabilities.

Contribute to AI platform architecture and engineering best practices.

QUALIFICATIONS

• 5+ years of AI/ML-focused software engineering experience.
• Strong Python engineering expertise.
• Production experience building LLM-powered applications.
• Experience with RAG, GraphRAG, embeddings, and vector search.
• Knowledge of ontology extraction and knowledge graph systems.
• Experience building REST APIs using FastAPI or similar frameworks.
• Cloud deployment experience with AWS, Azure, or GCP.
• Experience with Docker and containerized deployments.
• Familiarity with LLMOps/MLOps tooling and workflows.
• Strong understanding of AI evaluation frameworks and benchmarking.
• Excellent communication and collaboration skills.

PREFERRED EXPERIENCE

• Full-stack engineering experience.
• Experience mentoring engineering teams.
• Exposure to enterprise-scale AI deployments.
• Experience optimizing AI infrastructure performance.
• Background working within highly regulated enterprise environments.