AI Developer

• 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.