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Tier-1 employer, Bangalore metro, mid-level GenAI role and popular frameworks increase competition.
Specialized GenAI and regulated-finance context raises domain specificity but skills remain moderately transferable across industries.
Explicit 2+ years requirement plus mandatory Python, FastAPI, LangChain, Docker, and Kubernetes.
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Develop, deploy, and optimize agentic AI workflows and LLM orchestration pipelines using frameworks like LangChain and LangGraph.
Build scalable Python microservices and REST APIs with FastAPI to expose AI capabilities within Risk Modeling Solutions.
Implement and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases, and manage containerized deployment on Kubernetes with CI/CD integration.
Minimum 2+ years professional experience blending software development and data science/machine learning.
Strong Python development skills, experience with FastAPI and microservices architecture.
Proficiency in Docker, Kubernetes, and containerized deployment.
Hands-on experience with LLM orchestration frameworks (e.g., LangChain, LangGraph) and RAG architectures including vector databases (Pinecone, OpenSearch, etc.).
Experienced engineer with practical expertise in production AI/ML system deployment focused on risk or regulated enterprise environments.
Comfortable working across AI workflow development, backend API engineering, and cloud/container orchestration.
Familiar with NLP fundamentals, Transformer architectures, and ML observability tools such as Langfuse.