Role Overview:
We are looking for a Forward Deployed Engineer (FDE) who partners directly with UHG business teams to identify high-value problems and deliver AI-led automation and innovation under centralized council oversight.
An FDE in UHG is an empowered AI builder embedded within business units to understand real-world context, build practical solutions, and drive measurable AI driven outcomes.
The role blends hands-on engineering, solution architecture, product thinking, consulting, and customer-facing execution.
Key Responsibilities:
1. Business Embedding and Outcome Ownership
· Embed with business and engineering teams to own AI outcomes within a defined business domain.
· Build and deliver AI solutions hands-on; this is an execution role, not an advisory role.
· Convert AI potential into production value through code-first delivery and active repository contributions.
2. Problem Discovery and Solution Design
· Understand business processes, pain points, systems, data flows, and success metrics.
· Translate problems into MVPs, integrations, automations, and production-ready solutions with an ownership mindset
· Build across APIs, databases, cloud platforms, workflow tools, enterprise systems, and AI/GenAI technologies.
3. Rapid Prototyping and Value Validation
· Own the journey from discovery to working solution, rapidly proving business value through pilots and POCs
4. Integration, Adoption, and Scale
· Integrate with enterprise platforms, data systems, workflows, collaboration tools, and third-party APIs.
· Document architectures, implementation playbooks, reusable components, and customer-specific solution guides.
· Feed field learnings into product roadmap, accelerators, and go-to-market propositions.
Required Skills and Experience:
· 4–8 years of experience in AI led engineering, implementation, product, consulting, or customer-facing technology roles.
· Strong engineering fundamentals with hands-on coding experience in Python, JavaScript/TypeScript, Java, .NET/C#, or Go.
· AI proficiency is mandatory; candidates may come from software engineering, data science, UX, or related domains with proven hands on experience.
· Daily AI tool usage, demonstrable code contributions, and documented token usage.
· Strong analytical thinking and expertise in effectively utilizing data to derive AI solutions to solve business problems.
· Strong understanding of APIs, databases, cloud services, authentication, integrations, and deployment.
· Experience in Data and analytics platforms.
· Comfortable with structured and unstructured data.
· Experience with GenAI, LLMs, RAG, agents, AI workflow automation, prompt engineering, model integration and model training.
· Cloud experience across AWS, Azure, or Google Cloud.
· Good communication, adaptability, and problem-solving in ambiguous environments.
Good to Have:
· Knowledge of ML algorithms, model building, deployment, deep learning, and NLP.
· Experience integrating with Salesforce, Jira, Rally, Oracle, ServiceNow, Microsoft Dynamics or similar platforms.
· Familiarity with data engineering, ETL/ELT pipelines, BI dashboards, analytics, and reporting workflows.
· Healthcare exposure, especially contact centers, claims automation, finance, or technology services.