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Tier-1 brand and metro location but highly specialized GenAI skillset reduces applicant pool.
Strong GenAI and MLOps focus is transferable, though financial risk/governance preference increases domain specificity.
Explicit 7+ years plus mandatory deep GenAI, MLOps, cloud, containerization, and specific framework experience.
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Lead design and implementation of multi-agent AI workflows for risk management using frameworks like LangGraph, CrewAI, and Google ADK.
Translate complex risk domain business problems into scalable AI solutions including Retrieval-Augmented Generation (RAG) pipelines and LLM orchestration.
Develop and deploy containerized Python-based AI microservices with CI/CD on Kubernetes, ensuring observability and model quality evaluation.
7+ years of experience combining software development and machine learning/data science.
Expert-level Python development skills with experience building scalable backend APIs (FastAPI preferred).
Hands-on experience with multi-agent AI frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen) and RAG systems integrating vector databases.
Proven experience with containerization (Docker/Kubernetes) and cloud deployment (AWS, Azure, or GCP). Bachelor's degree in Computer Science, Engineering, Business, or related field.
Strong expertise in architecting complex, scalable AI solutions for regulated environments, preferably financial services.
Experience leading AI workflow observability and evaluation, including LLM evaluation harnesses and tracing platforms like Langfuse.
Deep familiarity with advanced NLP, transformer architectures, and integration of multi-agent AI systems within risk management frameworks.