AI Engineer

Technical Skills & Requirements

• AI Agents & Deep Agent Architectures

Frameworks: Expert-level mastery of LangGraph for building complex, cyclic, and stateful agentic workflows.

Deep Agents Harness: Hands-on experience implementing the Deep Agents open-source librarybuilt on top of LangGraph, leveraging its native architectural patterns.

Advanced Reasoning Patterns: Proven experience building agents with built-in dynamic task planning (to-do lists), isolated sub-agent spawning, and persistent virtual file systems within the graph state.

State Management & Interruption: Mastery over LangGraph state machines, including context compression middleware and the use of interrupt nodes to manage asynchronous human-in-the-loop (HITL) approval checkpoints.

• LLMOps, Evaluation & Guardrails

Evaluation Frameworks: Extensive experience with Ragas (Retrieval Augmented Generation Assessment) to programmatically benchmark faithfulness, context recall, and answer relevance.

Guardrails & Alignment: Implementation of strict security and behavior firewalls using frameworks like NeMo Guardrails, Guardrails AI, or Llama Guard.

LLM Telemetry: Experience mapping evaluation metrics into observability tools such as LangSmith, Phoenix, TruLens, or Arize.

• Advanced Retrieval (RAG) & Tool Integration

Vector Ecosystems: Experience with vector databases (e.g., Pinecone, Chroma, Milvus, Qdrant) and advanced retrieval techniques (hybrid search, cross-encoder re-ranking).

Deterministic Tool Calling: Expert-level Python or TypeScript proficiency to build robust agent function-calling mechanisms, integrating seamlessly with enterprise REST APIs, webhooks, and SQL/NoSQL databases.

• Production Deployment & Monitoring

Infrastructure: Deep understanding of containerized deployment (Docker, Kubernetes) within enterprise cloud environments (AWS, Azure, or GCP).

Auditability: Experience setting up structured logging and immutable telemetry pipelines (Datadog, Prometheus, ELK stack) for complete audit trails.