Are you building AI agents—not just API wrappers? ????

We’re looking for an Agentic AI Engineer / Data Scientist who loves designing intelligent, production-grade systems and turning LLM capabilities into reliable enterprise solutions.

What You’ll Work On

Architect and build single & multi-agent systems with complex orchestration and workflow design.

Develop production-grade RAG pipelines, semantic search, vector embeddings, and vector databases.

Build intelligent workflows covering data ingestion → retrieval → reasoning → tool calling → inference → response.

Engineer and optimize prompts, context strategies, and agent workflows for accuracy, reliability, and performance.

Design custom domain-specific guardrails to handle complex and non-trivial business scenarios.

Build robust LLM evaluations (Evals) to measure quality, accuracy, hallucination, and task performance.

Debug and trace agentic workflows using MLflow, LangSmith, or similar observability/evaluation platforms.

Continuously evaluate emerging AI frameworks and technologies and bring the right ones into production.

Tech Stack We’re Looking For

Must Have:

???? Python | ???? LangChain | ????️ LangGraph | ???? LLMs & Prompt Engineering

???? RAG | ???? Vector Embeddings & Semantic Search | ???? Agent Engineering

???? Multi-Agent Orchestration | ????️ Custom Guardrails | ???? LLM Evals

???? LangSmith / MLflow / Similar AI Observability Tools

???? You'll Be a Great Fit If You

Have 6 years of experience across Data Science, ML, AI Engineering, or related domains.

Have actually built and shipped agentic systems, not just experimented with LLM APIs.

Can think beyond individual models and design end-to-end AI system architectures.

Understand the trade-offs between accuracy, latency, cost, reliability, and scalability.

Enjoy solving ambiguous problems and turning them into production-ready AI solutions