Python AI Engineer

About the job

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 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. 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 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).