Senior Healthcare Forward Deployed Engineer

Accenture; Columbus (OH); On-site; USD 87,000 - 253,000; Full time

Job summary

Accenture is seeking a senior AI/engineering leader to drive forward-deployed AI programs in health plans, integrating claims data, provider data, and clinical workflows. You will shape architecture, ensure trust, privacy, and interoperability while guiding teams across multi-system environments.

You will design scalable AI solutions, oversee delivery, and partner with CIOs and health-plan leadership to drive strategic adoption and business outcomes.

Qualifications

• 8+ years hands-on engineering with cloud-native systems and AI platforms.
• 8+ years coding in Python/Java and deploying production systems.
• 3+ years delivering AI programs inside payer or healthcare environments.

Responsibilities

• Lead forward-deployed AI programs end-to-end inside health plans and payer operations.
• Define problem, outcomes, and success metrics for AI initiatives.
• Architect and govern AI programs across multi-system payer environments.

Skills

Cloud-native systems

APIs

Python/Java

CI/CD

Claude/OpenAI/Vertex AI

Agentic AI design

Education

Bachelor's degree or equivalent

Tools

Claude

OpenAI

Vertex AI

Job description

We Are:

The beginning of a new Data & AI decade that will reshape work and society has begun. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimize and reinvent their business with data & AI — backed by a $3B investment and commitment to our people to do industry-defining work.

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


• Minimum 8 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 8 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 3 years of experience delivering AI or technology programs inside payer organizations.


• Minimum 3 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 (https://www.accenture.com/us-en/careers/local/benefits)

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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