Join us as a Forward Deployed Engineer (FDE), where you’ll embed directly with our MDLive virtual care team to deliver impactful, production-grade AI solutions that enhance patient and provider experiences. This is a highly visible role where your work directly influences clinical outcomes, operational efficiency, and the future of AI in healthcare. As part of the AI Enablement Office (AIEO), you’ll combine technical depth with business partnership to accelerate responsible, scalable innovation.
What You’ll Do
Drive End-to-End AI Solution Delivery
- Own full lifecycle delivery of AI solutions—from problem framing through production deployment and adoption—aligned to MDLive business priorities
- Design and build AI applications using LLMs, RAG architectures, agentic workflows, and hybrid approaches tailored to clinical and operational workflows
- Translate ambiguous business problems into actionable technical solutions that deliver measurable outcomes
Embed with the Business and Deliver Value
- Partner directly with MDLive stakeholders to identify high-impact opportunities and rapidly iterate on solutions
- Operate in a sprint-based model with continuous feedback loops and visible progress toward defined outcomes
- Make informed technical tradeoffs, adapting scope and approach as business needs evolve
Lead Stakeholder Engagement
- Serve as the primary technical interface across product, engineering, and business teams
- Communicate AI capabilities, limitations, risks, and tradeoffs clearly to both technical and non-technical audiences
- Facilitate collaboration with security, legal, compliance, and data teams to ensure responsible and scalable delivery
- Mentor peers on practical AI delivery and production readiness
Build for Scale and Reuse
- Productionize solutions with strong observability, evaluation frameworks, and cost controls
- Extract repeatable patterns, components, and best practices to accelerate enterprise-wide adoption
- Document architectures and decisions to enable scalability and knowledge sharing
Required Qualifications
- 7+ years of experience in software engineering, data engineering, or AI/ML engineering with a track record of delivering production systems
- Hands-on experience building GenAI applications using LLMs, RAG pipelines, or agentic workflows
- Strong proficiency in Python and experience with at least one additional language (e.g., Java, TypeScript, Go, SQL)
- Experience working with cloud platforms (AWS, Azure, or GCP), including deployment, observability, and scalable systems
- Ability to translate business workflows into technical solutions in fast-paced, ambiguous environments
- Strong communication skills with the ability to engage both technical teams and business leaders