Serve as a technical leader and trusted authority on AI-assisted engineering, driving adoption of Copilot, code generation, and LLM-powered tooling across the engineering organization.
Architect and deliver AI-powered product features that improve healthcare outcomes—spanning intelligent automation, clinical decision support, and member-facing experiences.
Design and implement LLM workflows and prompt engineering patterns, ensuring reliable, safe, and scalable AI integrations in production environments.
Lead MLOps initiatives including model deployment pipelines, monitoring, versioning, and lifecycle management in collaboration with data science teams.
Build and maintain full-stack solutions using C# (.NET), React, and Python, with a strong focus on scalability, security, and maintainability.
Define and evangelize engineering best practices for AI integration, code quality, and system design across Staff and Senior engineers.
Collaborate with product, data, and clinical teams to identify high-impact opportunities for AI and machine learning in the healthcare domain.
Conduct design reviews, architecture discussions, and hands-on code reviews to raise the technical bar across the organization.
Mentor and grow senior engineers, fostering a culture of experimentation, ownership, and continuous learning.
Stay at the forefront of rapidly evolving AI/ML tooling, evaluating and recommending emerging technologies relevant to Monogram Health's mission.

Requirements

Deep proficiency in C# and .NET — including API design, microservices architecture, and enterprise-grade backend systems.
Strong hands-on experience with React and modern front-end development patterns (TypeScript, component architecture, state management).
Solid Python skills with practical experience in scripting, automation, data pipelines, or ML model integration.
Proven experience integrating LLMs (e.g., OpenAI, Azure OpenAI, Anthropic) into production applications via APIs, SDKs, or orchestration frameworks such as LangChain or Semantic Kernel.
Strong understanding of prompt engineering principles, RAG (Retrieval-Augmented Generation) patterns, and LLM evaluation strategies.
Hands-on experience with MLOps tooling and practices — model versioning, deployment, monitoring, and retraining pipelines.
Experience with AI-assisted development tools such as GitHub Copilot, Cursor, or equivalent, with the ability to coach teams on effective usage.
Solid understanding of cloud infrastructure (Azure preferred) and how to architect scalable, secure AI-integrated systems.
Strong command of software design principles — SOLID, clean architecture, domain-driven design, and API-first development.
Experience with CI/CD pipelines, infrastructure as code, and DevOps practices in a cloud-native environment.
Optional / Plus: Experience in healthcare technology, including knowledge of HL7, FHIR, or clinical data standards.
Familiarity with vector databases (e.g., Pinecone, Weaviate, Azure AI Search) for embedding-based retrieval systems.
Exposure to fine-tuning or customizing foundation models for domain-specific use cases.
Knowledge of responsible AI practices, model explainability, and bias mitigation in healthcare contexts.
Experience with data platforms such as Databricks, Snowflake, or Azure Synapse.

Benefits

Newfire Global is committed to consistent compensation practices across our organization.
Total compensation for this role is market driven.
Comprehensive benefits package including medical, dental & vision coverage.
Health spending accounts.
Voluntary benefits.
Leave of absence policies.
Employee Assistance Program.
401(k) program with employer contribution.
Flexible work schedules and time-off policy.
Company equipment for all new full-time US-based remote employees.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

C#.NETReactPythonAPI designmicroservices architectureMLOpsprompt engineeringCI/CDdata pipelines

Soft Skills

technical leadershipmentoringcollaborationcommunicationcontinuous learningproblem-solvingownershipexperimentationdesign reviewarchitecture discussion