Agentic AI Engineer

4 6 years software engineering experience, including 2+ hands-on experience building LLM-based applications in production.

Strong Python (TypeScript a plus); deep familiarity with LLM APIs (OpenAI, Anthropic, Gemini, open-weight models) and prompt/context engineering.

Hands-on experience with agent frameworks, tool/function calling, structured outputs, and multi-agent orchestration patterns.

Experience with vector databases (Pinecone, Weaviate, pgvector, etc.), embeddings, and retrieval system design.

Solid backend fundamentals: APIs, async systems, distributed services, Docker/Kubernetes, and cloud platforms.

Practical knowledge of LLM evaluation, safety, and reliability techniques; comfort working with imperfect, probabilistic systems.

Excellent communication skills and a track record of ownership in fast-moving environments.

Good-to-Have

Fine-tuning / RLHF experience, open-source contributions to AI tooling, or published work on agents.

Experience with MLOps/LLMOps platforms (LangSmith, Langfuse, Weights & Biases, MLflow) and enterprise security/compliance requirements.

Architect and build multi-step, tool-using LLM agents (planning, memory, retrieval, orchestration) for production use cases.

Design robust agent workflows using frameworks such as LangGraph, CrewAI, AutoGen, or the OpenAI/Anthropic agent SDKs; integrate tools via MCP and function calling.

Collaborate with solution architects, product teams, and application owners to implement integration designs.

Build RAG pipelines, vector search, and context-management strategies that keep agents accurate and cost-efficient.

Strong Python (TypeScript a plus); deep familiarity with LLM APIs (OpenAI, Anthropic, Gemini, open-weight models) and prompt/context engineering.