Python AI Engineer - RAG Pipelines & Autonomous Agents

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

Responsibilities

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 FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps 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 OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• 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.

Requirements (Ideal Candidate)

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).