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Tier-1 bank and Bangalore location increase competition, but senior specialized GenAI skills narrow the candidate pool.
Specialized GenAI for risk with governance and regulated-finance expectations limits cross-industry transferability.
Explicit 10+ years, mandatory GenAI/LLM, RAG, Kubernetes, and cloud experience enforce strict filters.
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Lead design and implementation of complex multi-agent AI workflows using frameworks like LangGraph, CrewAI, and Google ADK for risk management applications.
Develop and deploy cost-efficient, scalable AI solutions including backend microservices and RAG pipelines integrated with vector databases.
Manage AI system deployment and operations involving containerization (Docker/Kubernetes), CI/CD pipelines, observability, and evaluation of model performance.
10+ years of experience combining software development with data science/machine learning.
Expert Python skills with experience in scalable backend services and REST APIs, preferably using FastAPI.
Proven experience architecting multi-agent AI frameworks, RAG systems, vector database integration, and cloud-based containerized AI deployments (AWS, Azure, or GCP).
Bachelor's degree in Computer Science, Engineering, Business, or related field.
Experienced individual contributor who can translate complex risk domain problems into AI technical solutions effectively.
Strong background in ML, deep learning, NLP, and AI governance with hands-on expertise in multi-agent AI system design and deployment.
Capability to optimize AI solutions for cost and performance, with knowledge of tokenomics, caching strategies, and enterprise AI risk requirements.