Artificial Intelligence Engineer

Role & responsibilities

Build and productionize AI capabilities LLM applications, RAG systems and agentic AI workflows. This is a hands-on engineering role, not research or prompt engineering alone.

• Build LLM-powered enterprise applications and AI/ML components
• Develop RAG pipelines: document ingestion, chunking, embedding, retrieval
• Build agentic workflows, tool-using agents and multi-step orchestration
• Implement tool/function calling with controlled, permission-aware execution
• Integrate models and agents with enterprise APIs, applications and data platforms
• Build evaluation frameworks retrieval quality, groundedness, task completion
• Implement guardrails, human-in-the-loop controls, logging, tracing and observability

Preferred candidate profile

2–5+ years in software, ML or AI engineering, with production AI/ML or agentic applications actually shipped.

• Strong Python and software engineering fundamentals
• Solid grasp of LLM application architecture, RAG patterns, embeddings and vector search
• Experience designing AI evaluation and working with model/agent observability
• Able to debug AI systems beyond prompt changes
• Understanding of AI security, guardrails and safe tool access
• Exposure to LangChain, LangGraph, LlamaIndex, Hugging Face, PyTorch, MLflow, and vector DBs such as Milvus, Pinecone, Weaviate or pgvector
• Enterprise implementation experience preferred

Strong applied AI experience can offset fewer total years.