MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Principal Machine Learning Engineer leading AI and data science strategy at Wiser Solutions. Architecting production AI systems and mentoring a team of engineers while delivering measurable outcomes.
Responsibilities
- Define and evolve Wiser's AI and data science technical strategy in partnership with product and business leadership
- Represent Wiser's AI capabilities to customers, partners, and advisors—articulating our approach, roadmap, and differentiation
- Identify high-impact opportunities where AI can solve customer problems or create competitive advantage
- Establish technical standards, patterns, and best practices that influence engineering decisions across the organization
- Architect and build production AI systems including LLM applications, RAG pipelines, semantic search, and traditional ML models
- Design rigorous evaluation frameworks, experimentation methodologies, and monitoring systems that ensure AI solutions deliver reliable, measurable results
- Bridge classical data science approaches (statistical modeling, experimentation design, feature engineering) with modern generative AI techniques
- Own technical quality for AI systems end-to-end: from data pipelines through model deployment to production observability
- Partner with product management to translate business requirements into technical approaches and validate solutions against customer needs
- Mentor and elevate the AI/data science team (3-5 engineers), raising the technical bar through code review, architecture guidance, and knowledge transfer
- Collaborate across engineering teams to integrate AI capabilities into Wiser's broader platform architecture
- Drive build-vs-buy decisions and vendor evaluations for AI infrastructure and tooling
- Champion AI-native development practices across Wiser engineering—demonstrating how AI tools accelerate development, improve code quality, and change what's possible
Requirements
- 15+ years of experience in data science, machine learning, or ML engineering
- Deep expertise in statistical methods, experimental design, and classical ML (not just LLM integration)
- Proven ability to architect and deliver production ML/AI systems at scale on cloud platforms (AWS strongly preferred)
- Strong software engineering fundamentals: you write production-quality code, not just notebooks
- Track record of organization-wide technical influence—setting standards, driving architectural decisions, mentoring engineers
- Experience communicating technical strategy and capabilities to non-technical stakeholders, including customers and executives
- Demonstrated ability to operate autonomously, identify high-impact problems, and drive initiatives without close direction
- Active, daily use of AI coding assistants (Cursor, Claude Code, GitHub Copilot) and a demonstrated belief that AI fundamentally changes how software gets built.
Benefits
- Performance-based discretionary bonuses and variable pay plans are available for some positions
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