Summary
A client of Coretek Labs is immediately hiring for a Python AI Engineer – RAG Pipelines & Autonomous Agents
Title: Python AI Engineer – RAG Pipelines & Autonomous Agents
Position type: Full Time/ Contract
Location: Hybrid/Remote
Python AI Engineer – RAG Pipelines & Autonomous Agents, You will:
Generative Agentic AI Engineering
Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.
Python Enterprise AI Engineering
Build high performance Python microservices and AI APIs using Fast API, Flask, Lang Chain, Llama Index, and MCP SDKs.
Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.
Technologies Ecosystem
Use Jupyter Notebooks, Tachyon, and enterprise Gen AI platforms for experimentation and model refinement.
Leverage Git Hub Copilot and modern Dev Sec Ops workflows to accelerate development.
Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.
Cloud Platform Engineering
Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
Containerize and orchestrate AI services using Red Hat Open Shift and enterprise Kubernetes.
Build and manage CI/CD pipelines aligned to Dev Ops best practices.
Data Integration & Retrieval
Work with vector databases (Mongo DB Atlas Vector Search, Chroma, Pinecone, Redis, pg Vector).
Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.
Architecture, Governance & Delivery
Participate in architecture reviews and contribute to compliant AI governance frameworks.
Ensure adherence to risk, security, and regulatory standards.
Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.
The ideal candidate will have:
Experience building LLM generative AI or agentic AI systems.
Experience in Python (async programming, APIs, microservices, distributed systems).
Experience with GCP and Open Shift/Kubernetes for scalable AI deployments.
Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
Experience with Jupyter, Tachyon, Git Hub Copilot, CI/CD, and modern Dev Ops tooling.
Demonstrated ability to work independently and drive AI innovation.
Familiarity with Lang Chain, Llama Index, and APIs for Open AI, Google Gemini, or similar models.
