Software Engineering Advisor – Forward Deployed Engineer (FDE)

Cigna Group•Louisville, KY

21h•Remote

About The Position

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.

Requirements

5+ 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
Experience working across teams and influencing without direct authority

Nice To Haves

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

Responsibilities

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