Staff Engineer / Principal Engineer

Role Overview

• 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 the client.

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.

Tech Stack

• Azure
• Cloud
• Microservices
• Python
• React
• TypeScript
• .NET

Benefits

• 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