Staff AI Enablement Engineer
New
J
JobgetherHealth Technology
Based in United StatesFull-TimeStaff
Salary190,000 - 230,000 USD per year
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Job Details
- Experience
- 8+ years of professional software engineering experience
- Required Skills
- Machine LearningSoftware EngineeringPrompt EngineeringLLMGenerative AI
Requirements
- 8+ years of professional software engineering experience with end-to-end production systems.
- Hands-on experience building production applications with LLMs and agentic systems.
- Expertise in prompt engineering, context/tool design, evaluation, orchestration, and operationalization.
- Production-level experience with AI coding assistants and agentic developer tools.
- Working knowledge of applied ML fundamentals, including features, evaluation, and model selection.
- Proven ability to influence technical adoption across an organization without formal authority.
- Strong product instincts for internal developer tooling and user engagement.
- Excellent written and verbal communication skills for presenting to engineering leadership.
- Experience operating at staff or principal level with cross-team scope.
- Experience with vector search, RAG, and LLM observability (advantageous).
- Experience in healthcare or regulated industries (preferred).
Responsibilities
- Serve as the engineering subject-matter expert for generative AI, agentic systems, and automation.
- Meet with engineering teams to identify high-friction areas and prioritize AI-enabled solutions.
- Own the AI enablement roadmap from strategy through execution, measurement, and communication.
- Evaluate and guide adoption of AI coding assistants and agentic developer tools.
- Design, build, and operate a scalable network of specialized automation agents.
- Establish shared architectures and reusable patterns for efficient AI agent deployment.
- Instrument AI solutions to measure outcomes such as time saved and quality improvements.
- Partner with stakeholders to modernize rules-based systems into ML-informed solutions.
- Define practical AI adoption frameworks, guardrails, and operational reliability standards.
- Promote AI best practices through documentation, workshops, and hands-on collaboration.
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