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Provider

An AI Native Engineer that brings deep technical and domain expertise to AI programs at provider organizations - designing solutions that work inside the realities of the electronic medical record and clinical workflows, extending to the administrative side of care where AI can ease the burden of documentation, prior authorization, coding, and revenue cycle. You'll help providers put AI to work across both the clinical and operational realities of their environment and help define the future of healthcare interoperability.

Payer

An AI Native Engineer that brings 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:

• Embed with provider teams. Work forward-deployed inside health systems, hospitals, and physician groups - alongside clinical informatics, physicians and nurses, and revenue-cycle and operations leaders - redesigning processes around AI and co-creating solutions using AI tooling to move quickly from idea to working solution.
• Scope the program. Define the problem, the intended outcome, and what good looks like - and identify where AI can automate or augment clinical and administrative work.
• Set the technical architecture. Lead the technical architecture of AI programs in provider environments and articulate it clearly, on paper and in diagrams, for engineers and clinical leaders alike.
• Design for trust. Make sure the solutions you shape are safe, compliant, and clinically sound - building the human oversight, transparency and PHI safeguards that AI in care settings demands.
• Reimagine the ways of working. Reimagine the future way of working for clinical and operational teams, rather than simply automating today's processes.
• Shape interoperability. Help define the future of healthcare interoperability - how the broader ecosystem can connect to the health record in a deep, meaningful way.
• Turn engagements into reusable approaches. Distill what works across providers into repeatable methods and accelerators, so each program starts further ahead than the last.

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 3 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
• Minimum of 3 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
• Minimum of 3 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
• Minimum of1 year of experience delivering AI or technology programs inside payer organizations.
• Minimum of 1 year of 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, or minimum 6 years' work experience with Associate's degree) in Computer Science, Computer Engineering, or a related field.

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 10/31/2026.

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 $70,350 to $235,100

Colorado $63,800 to $203,100

Connecticut $63,800 to $203,100

District of Columbia $68,000 to $216,300

Illinois $59,100 to $203,100

Maine $54,400 to $173,100

Maryland $63,800 to $203,100

Massachusetts $63,800 to $216,300

Minnesota $63,800 to $203,100

New York $66,300 to $235,100

New Jersey $68,000 to $235,100

Ohio $59,100 to $188,100

Virginia $59,100 to $216,300

Washington $80,200 to $216,300