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Tier-1 brand and metro location increase competition, but senior, highly specialized ML/GenAI requirements moderate density.
Requires deep GenAI, RAG, and risk modeling experience, making skills less transferable across industries.
Explicit 10+ years plus deep GenAI, MLOps, cloud, and governance requirements make filters very strict.
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Lead design and implementation of complex multi-agent AI workflows using frameworks like LangGraph, CrewAI, and Google ADK within risk management.
Develop end-to-end AI solutions including backend microservices and APIs, focusing on Retrieval-Augmented Generation (RAG) pipelines and optimization for cost and performance.
Deploy and maintain containerized AI services on Kubernetes with CI/CD pipelines, ensuring observability, evaluation, and governance of models.
10+ years of professional experience blending software development and data science/machine learning.
Expert-level Python development skills with experience building scalable backend services and REST APIs (FastAPI preferred).
Proven experience with multi-agent AI frameworks (e.g., LangChain, LangGraph), RAG systems, vector databases, containerized deployments (Docker/Kubernetes) on cloud platforms (AWS, Azure, or GCP).
Bachelor's degree in Computer Science, Engineering, Business, or related field.
Significant expertise in architecting LLM-based AI systems emphasizing risk, safety, and enterprise governance within regulated industries like financial services.
Strong operational focus on cost-effective AI architectures via tokenomics, model-tiering, caching, and performance optimization.
Experience in building observability and evaluation frameworks for LLMs, including using tools like Langfuse and implementing MLOps practices.