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With over 45,000 professionals dedicated to Data & AI, Accenture's Data & AI organization brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem.
You are:Bring deep technical and domain expertise to AI programs at health plans - designing solutions that work inside the realities of core administrative platforms and the claims and member data that flow through them, where AI can ease the burden of adjudication, prior authorization, utilization management, and member and provider service. You'll help payers put AI to work across both the operational and clinical realities of their environment and help define the future of healthcare interoperability.
The Work:- Lead forward-deployed pods. Own AI programs end-to-end inside health plans, PBMs, and payer operations - directing multiple concurrent workstreams and aligning claims operations, utilization and care management, network and provider data, and analytics and actuarial leaders around outcomes, from architecture through adoption.
- Scope the program. Define the problem, the intended outcome, and what good looks like - and identify where AI can automate or augment administrative and clinical work.
- Set and govern the technical architecture. Architect and govern AI programs across payer environments at program scale - spanning multi-system integration across core administrative, claims, and clinical platforms - and articulate it clearly, on paper and in diagrams, for engineers, business and clinical leaders, and executives alike.
- Design for trust. Make sure the solutions you shape are safe, compliant, and fair - building in the human oversight, transparency, and PHI safeguards that AI touching coverage and payment decisions demands.
- Reimagine the way of working. Reimagine the future way of working for operations and clinical teams, rather than simply automating today's processes.
- Shape interoperability. Help define the future of healthcare interoperability - how plans, providers, and the broader ecosystem can connect around claims and clinical data in a deep, meaningful way.
- Lead and develop the team. Set delivery standards and pace, coach junior FDEs through co-delivery, and own performance management and growth for the engineers in your pod.
- Shape strategy with executives. Partner with client CIO, CFO, Chief Medical Officer, and health-plan leadership to shape AI reinvention strategy - building value architecture, ROI backlogs, use-case prioritization frameworks, and multi-year adoption roadmaps, with commercial metrics attached.
- Build the practice. Define and publish reusable methods, blueprints, and accelerators that scale across payer engagements - codifying delivery learnings and engineering standards that grow the FDE practice and develop the next generation of forward-deployed engineers.
Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.
Here's what you need- Minimum of 8 years of hands-on engineering experience with cloud-native systems - APIs, microservices, containerization, and serverless - including working with AI platforms (Claude, OpenAI, Vertex AI, or open-source models) and at least 1 year designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production
- Minimum8 years of experience hands-on coding in at least one general-purpose language (e.g., Python, Java) and the ability to pick up others as engagements demand
- Minimum8 years of experience with hands-on production deployment experience - CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging - with demonstrated end-to-end delivery ownership in a client-embedded environment
- Minimum3 years of experience delivering AI or technology programs inside payer organizations.
- Minimum3 years hands-on experience using Claude or an equivalent frontier AI tool to do the work.
- Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have minimum 6 years work experience)
Professional Skills Requirements:- Deep understanding of how core administrative and claims platforms work and how claims and member data are structured, with direct QNXT, Facets, HealthEdge, or comparable experience
- Fluency with data - non-trivial SQL, relational modeling, and reasoning about data quality and data lineage
- Working knowledge of healthcare interoperability standards and EDI mechanisms: X12 transaction flows (837, 835, 270/271, 276/277, 278, 834), FHIR APIs, and the broader exchange landscape
- Deep understanding of payer workflows - how coverage and payment actually work across eligibility and enrollment, claims adjudication, prior authorization and utilization management, provider network and reimbursement, appeals and grievances, and risk and quality programs - including a real sense of where administrative burden and cost accumulate and where AI can meaningfully relieve it, and how the regulatory environment (HIPAA and CMS requirements) shapes what solutions can look like
- AI solution architecture and AI program scoping, with strong written and presentation skills
- Comfortable working forward-deployed, embedded directly with health plans in a fast-iterating model
- High tolerance for ambiguity and the patience to make things work inside slow-moving, high-stakes IT environments
Technical fundamentals:The technology fundamentals expected of any capable full-stack engineer building AI solutions in a healthcare environment:
- How the web works end to end: HTTP/HTTPS, the request/response lifecycle, REST APIs, and JSON.
- API design and integration: authentication, pagination, rate limits, retries, versioning, and event-driven patterns (webhooks, batch file feeds); comfortable reading and building against unfamiliar API documentation, including FHIR and payer platform and clearinghouse APIs.
- Data fundamentals: relational and NoSQL databases, data modeling, and SQL - applied to the messy, real-world claims, eligibility, and clinical data these programs run on.
- Working with frontier AI: the fundamentals of building with large language models - prompting, grounding responses in real data (retrieval / RAG), structured outputs and tool use, and evaluating quality while designing around non-determinism and the need for human oversight.
- Security and handling PHI: authentication vs. authorization, token-based auth (OAuth / JWT), validating all input (never trust the client), and the essentials of protecting sensitive health data - encryption in transit and at rest, least-privilege access, and audit logging.
- A practical feel for system tradeoffs: caching, queues and background jobs, idempotency, and when work should run in-request versus asynchronously.
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 11/30/2026.
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
U.S. Employee Benefits | Accenture
Role Location Annual Salary Range
California $94,400 to $316,300
Colorado $94,400 to $273,200
Connecticut $94,400 to $273,200
District of Columbia $100,500 to $291,000
Illinois $87,400 to $273,200
Maine $80,400 to $232,800
Maryland $94,400 to $273,200
Massachusetts $94,400 to $291,000
Minnesota $94,400 to $273,200
New York $87,400 to $316,300
New Jersey $100,500 to $316,300
Ohio $87,400 to $253,000
Virginia $87,400 to $291,000
Washington $100,500 to $291,000
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