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+ yearsof 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
- Experience working across teams and influencing without direct authority
Preferred Qualifications
- Experience in healthcare, telehealth, or regulated industries (e.g., insurance, pharmacy, care delivery)
- Familiarity with HIPAA-aligned practices, responsible AI governance, and model risk management
- Experience in forward-deployed, solutions engineering, or technical advisory roles
- Experience with enterprise AI platforms such as Azure OpenAI, AWS Bedrock, or Vertex AI
- Understanding of responsible AI principles including privacy, bias mitigation, explainability, and human oversight
What Success Looks Like
- Trusted partner to the MDLive team with sustained and expanded engagement
- Consistent delivery of high-quality AI solutions that drive measurable business value
- Strong ownership of workstreams from design through handoff
- Reusable solutions and patterns adopted across teams
- Delivery that meets enterprise standards for security, privacy, and responsible AI
If you will be working at home occasionally or permanently, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 10Mbps download/5Mbps upload.
