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Job Description & How to Apply Below
Key Responsibilities
- Design and implement scalable RAG-based GenAI systems
- Architect and optimize vector database solutions for semantic search and retrieval
- Integrate and work with MCP (Model Context Protocol) for standardized LLM tool and context management
- Build and orchestrate agentic AI workflows using LLMs
- Lead end-to-end AI solution development from prototype to production
- Develop robust APIs using FastAPI and Python. Ensuring security and scalability
- Deploy and manage AI services using Docker
- Establish best practices for code quality, version control, and CI/CD using Git
- Guide and mentor junior and senior engineers
- Collaborate with ML, data, and cloud teams to drive innovation
Must-Have Skills
- Strong hands-on experience with RAG (Retrieval-Augmented Generation)
- Expertise in Vector Databases (e.g., FAISS, Pinecone, Weaviate, Milvus)
- Experience building Agentic AI systems
- Advanced proficiency in Python
- Experience with FastAPI
- Containerization using Docker
- Version control using Git
- Solid foundation in Machine Learning / Deep Learning
Good-to-Have Skills
- Experience with Azure Cloud
- LLM fine-tuning (LoRA, PEFT, instruction tuning, etc.)
- Orchestration using Kubernetes
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