Healthcare Forward Deployed Engineer

Accenture; Chicago (IL); On-site; USD 140,000 - 180,000; Full time

Job summary

Accenture seeks an AI Native Engineer to work embedded with health providers and payers, shaping AI solutions across clinical and admin workflows. You will design architecture, ensure PHI/compliance, and drive interoperability within healthcare ecosystems.

The role requires hands-on cloud-native engineering, AI tooling experience, and ability to deliver AI programs in payer environments with strong collaboration across clinical and IT teams.

Qualifications

• 3+ years hands-on engineering with cloud-native systems plus AI platforms and agents in production.
• 3+ years coding in Python or Java and ability to learn other languages quickly.
• 3+ years of production deployment experience — CI/CD, IaC, monitoring, debugging with ownership in a client setting.
• 1+ year delivering AI programs inside payer organizations.
• 1+ year using Claude or frontier AI tools for real work.

Responsibilities

• Embed with provider teams, redesigning processes around AI within health systems.
• Scope the program by defining problems, outcomes and success criteria.
• Set the technical architecture of AI programs in provider environments.
• Design for trust with PHI safeguards, safety and compliance.
• Reimagine future work for clinical and operational teams.
• Shape interoperability to connect health records with AI capabilities.
• Turn engagements into reusable methods and accelerators for faster start-up.

Skills

AI platforms

Agentic solutions

Python

Java

CI/CD

Terraform/Helm

Education

Bachelor's in CS/CE or related

Tools

Claude

OpenAI

Vertex AI

Terraform

Helm

Jenkins

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.

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:

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 / R