4.0 - 6.0 years

8 - 12 Lacs

Kalyani, Nagar, Pune

Work from Office

Design and build AI-powered applications such as intelligent agents, chatbots, and domain-specific copilots using LLMs. Work with LLMs (OpenAI, Cohere, Hugging Face, etc.) and integrate them via APIs or custom pipelines. Create semantic search experiences using embedding generation and chunking strategies for enterprise documents. Implement RAG (Retrieval-Augmented Generation) pipelines using Vector Databases like Pinecone , Weaviate , FAISS , or Chroma . Build and expose REST APIs using FastAPI , Flask , or Django , integrating LLM and retrieval components. Manage and optimize backend systems using PostgreSQL , MongoDB , and Redis . Collaborate with AI/ML engineers, data engineers, and product teams to translate BFSI requirements into working solutions. Apply best practices in API security, caching, observability, and scaling for AI apps. RequiredSkills & Experience: 4 to 6 years of backend development experience with Python 3.x . Proficiency with web frameworks like FastAPI , Django , or Flask . Strong experience with PostgreSQL , MongoDB , and Redis . Solid understanding of LLM-based applications prompt engineering, chat workflows, and model fine-tuning/inference (preferred). Experience with creating embeddings , chunking text, and building semantic search pipelines. Familiarity with Vector DBs like Pinecone , Weaviate , Chroma , or FAISS . Ability to implement or contribute to RAG pipelines . Experience with API integration and orchestration for hybrid AI systems. Good understanding of version control (Git) and containerization using Docker . Nice toHave: Prior experience delivering AI/ML or chatbot solutions for BFSI use cases. Exposure to tools like LangChain , Haystack , or LlamaIndex . Working knowledge of cloud platforms (AWS/GCP/Azure) and AI-specific services. Familiarity with CI/CD pipelines and DevOps processes.

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