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Tier-1 employer, metro locations, and broad senior AI skillset drive high candidate competition.
Specialized AI/LLM expertise is transferable across industries, though financial services and governance needs increase domain sensitivity.
Explicit 8+ years, 3+ years ML, and many mandatory tech/domain skills make filters highly strict.
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Design and build production-grade AI/ML subsystems including agentic workflows, LLM applications, evaluation pipelines, and MLOps tooling aligned with enterprise architectural vision.
Own technical delivery of AI solutions ensuring security, scalability, and performance with minimal supervision; collaborate cross-functionally and communicate complex information effectively.
Mentor junior engineers, lead or advise on policies and operational effectiveness related to AI technology within the organization.
Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related discipline.
8+ years software engineering experience including at least 3 years building AI/ML systems or scalable ML infrastructure at enterprise scale.
Proficiency in Python (FastAPI, asyncio) and/or Go/Java with experience building high-throughput, low-latency APIs and microservices.
Experience with GenAI/LLM systems, distributed systems architecture, AWS services (EC2/EKS, S3, IAM, RDS, AI services), Docker/Kubernetes, and CI/CD pipelines.
Experienced in developing complex agentic AI systems including multi-agent orchestration and advanced frameworks (Strands, LangGraph, Google ADK) with prompt engineering and RAG architecture knowledge.
Comfortable working independently from system design through delivery, influencing senior stakeholders and collaborating cross-functionally in large enterprise settings.
Background in cloud-native architecture, scalable ML infrastructure, and possibly financial services regulatory environment, with ability to lead and mentor technical teams.