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Strong Tier-1 brand and metro location increase candidate density despite niche senior ML platform requirements.
Deep ML/LLM platform and SDK ownership required, making skills highly domain-specific and less transferable.
Explicit 8+ years plus mandatory platform/SDK ownership and specific LLM/tech stack enforce strict shortlisting filters.
Own and architect GE HealthCare's internal AI engineering platform including shared libraries, interfaces, code generation tools, and automated verification systems used by data scientists for production AI delivery.
Lead hands-on development of key platform components — agent execution layer, secure data resolution, configuration-driven UI — ensuring backward compatibility and migration support across evolving applications.
Design and enforce automated engineering standards to prevent runtime failures, manage versioning, and maintain reliability, security, and compliance at scale without heavy manual review.
Bachelor's degree in Computer Science, Software Engineering or related field, or equivalent practical experience.
Minimum 8 years professional software engineering experience with demonstrable ownership of shared libraries, frameworks, SDKs, or internal platforms depended upon by engineering teams.
Expert Python and strong TypeScript skills; experience with API design/versioning, relational databases and CI/CD pipelines.
Hands-on experience with multiple AI agent frameworks (e.g., LangGraph, LangChain, AWS Bedrock AgentCore) and substantial production experience with LLM systems; strong knowledge of major public cloud (ideally AWS).
Senior-level engineer with deep experience building and maintaining internal platforms or SDKs critical to other engineering teams, not just application development.
Strong architect with ability to define machine-readable interfaces and design for long-term maintainability and backward compatibility under evolving dependencies.
Experienced in cross-team collaboration and able to communicate technical trade-offs effectively to executives, security/risk teams, and data scientists.