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Tier-1 employer and Bangalore location increase competition, but specialized GenAI skillset narrows candidate pool.
Requires specialized GenAI/LLM and regulated risk-domain experience, so cross-industry transferability is low.
Multiple mandatory requirements (6+ years, deep GenAI, RAG, Docker/K8s, cloud) create high shortlisting strictness.
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Design and lead implementation of complex multi-agent AI workflows for advanced reasoning and autonomous execution using frameworks like LangGraph, CrewAI, and Google ADK.
Develop robust, end-to-end AI and Retrieval-Augmented Generation (RAG) solutions addressing complex business problems within risk management, integrating vector search databases and LLM orchestration.
Build, deploy, and maintain scalable Python-based backend microservices and APIs with containerized AI systems on cloud platforms including CI/CD and observability for model evaluation and performance.
6+ years of combined software development and data science/machine learning experience.
Expert-level Python development skills with proven backend service and API (FastAPI preferred) design and implementation.
Hands-on experience with agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen) and RAG architectures integrating vector databases (e.g., Pinecone, OpenSearch).
Bachelor's degree in Computer Science, Engineering, Business, or related field.
Experienced individual contributor with a strong focus on AI system architecture, especially multi-agent frameworks and LLM orchestration within risk management contexts.
Proficient in cloud-native AI deployments using Docker, Kubernetes, including CI/CD pipelines and observability platforms like Langfuse for AI workflow tracing.
Familiarity with financial services or regulated industries, and strong understanding of AI risk, safety, and governance in enterprise environments.