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Tier‑1 brand, metro locations, and mid-level generalist lead title increase candidate competition.
ML/LLM production skills are transferable, but regulated finance controls raise domain sensitivity.
Explicit 5+ years, mandatory LLM experience, cloud/tech stack and regulated environment create strict filters.
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Design, develop, and troubleshoot LLM-powered applications and multi-agent workflows supporting regulated business functions.
Drive adoption and governance of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes with measurable validation standards.
Provide L3 support for LLM production systems including incident management, model rollouts, and ensure high availability, reliability, and compliance with SLAs.
5+ years of applied software engineering experience; minimum 1 year with LLM-enabled systems in regulated environments.
Strong coding skills in Java, Python, Athena, and SQL with experience building LLM-enabled microservices and retrieval pipelines.
Hands-on experience with AWS cloud technologies (Redshift, Dynamo DB, Aurora, Databricks) and secure secret management.
Formal training or certification in software engineering concepts; strong understanding of data modeling, embeddings, LLM risk profiles, and responsible AI use.
Experience leading enterprise adoption of AI-assisted software development tools and setting team validation standards for AI outputs.
Proven ability to design and operate LLM systems meeting reliability, security, and regulatory requirements in a complex, regulated environment.
Strong system design and operational skills for LLM architectures including retrieval layers, vector stores, caching, and observability, plus L3 support expertise.