Senior Healthcare Forward Deployed Engineer
for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content accordingly.
We need to include sections only when posting has real specific content. So we can have sections: About the role (maybe from "We Are:" and "You are:"?), Responsibilities (The Work), Requirements (Here's What You Need), Qualifications (maybe same as Requirements?), Skills (Professional Skills Requirements), Benefits (benefits description), Pay (salary ranges), Schedule (travel may be required). Also maybe include location? Not required.
We must not include boilerplate like EEO, accommodation, etc. Remove those.
We must preserve all substantive information; do not invent or summarize away detail. So we need to keep details like bullet points.
We need to use - for any list of duties/requirements/benefits, and
for prose.
We need to avoid placeholder text like "commensurate with experience". Not present.
We need to output only HTML fragment, no /, no markdown fences.
Let's extract content.
We Are: paragraph about Accenture's Data & AI decade.
You are: paragraph about role description.
The Work: bullet-like paragraphs each starting with bold? Actually it's sentences. We'll need to convert each duty into list items.
Here's What You Need: list of minimum requirements.
Professional Skills Requirements: list of skills.
Compensation: paragraph about compensation varies, plus salary ranges per location.
Benefits: paragraph about benefits.
Travel may be required: schedule.
We need to map sections:
- About the role: combine "We Are:" and "You are:" maybe as prose.
- Responsibilities: The Work bullet points.
- Requirements: Here's What You Need (minimum years etc.)
- Qualifications: maybe same as Requirements? But we can treat Requirements as qualifications. However we need both sections if content distinct. Could combine? The instruction: prefer a few substantive sections over many thin ones. So maybe we have About the role, Responsibilities, Requirements (including qualifications and skills), Benefits, Pay, Schedule. But they asked to use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can include those that have content.
We have content for About the role, Responsibilities, Requirements (minimum experience), Qualifications (maybe degree etc.), Skills (Professional Skills Requirements), Benefits, Pay (salary ranges), Schedule (travel). So we can include all.
We must not repeat job title as heading.
Let's craft.
First, About the role: combine We Are and You are paragraphs.
We Are: "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: "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."
Combine into prose.
Responsibilities: The Work section bullet points. Let's list each sentence as list item.
The Work sentences:
- 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.
We might separate travel into Schedule section later.
Requirements: Here's What You Need bullet points.
List:
- 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)
Qualifications: maybe same as Requirements? But we could treat Qualifications as degree requirement? However we already have degree in Requirements. Could split: Requirements = experience, Qualifications = education. Let's do that.
Thus Requirements: experience bullets (first five). Qualifications: degree bullet.
Skills: Professional Skills Requirements list.
Let's extract each bullet:
- 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.
We'll need to split long sentences into list items? Each bullet is a skill; we can keep as
- each.
Benefits: "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"
We should keep the sentence, maybe as
.
Pay: Compensation paragraph plus salary ranges.
Compensation paragraph: "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."
Then list of location salary ranges. We'll need to present as list.
Schedule: Travel may be required paragraph.
Now we need to output HTML with headings
for each section.
We must not include any markdown fences.
Let's craft.
Structure:
About the role
...combined...
Responsibilities
- ...
...
Requirements
- ...
...
Qualifications
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
Skills
- ...
...
Benefits
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage
for prose. We need to avoid placeholder text like "commensurate with experience". Not present. We need to output only HTML fragment, no /, no markdown fences. Let's extract content. We Are: paragraph about Accenture's Data & AI decade. You are: paragraph about role description. The Work: bullet-like paragraphs each starting with bold? Actually it's sentences. We'll need to convert each duty into list items. Here's What You Need: list of minimum requirements. Professional Skills Requirements: list of skills. Compensation: paragraph about compensation varies, plus salary ranges per location. Benefits: paragraph about benefits. Travel may be required: schedule. We need to map sections: - About the role: combine "We Are:" and "You are:" maybe as prose. - Responsibilities: The Work bullet points. - Requirements: Here's What You Need (minimum years etc.) - Qualifications: maybe same as Requirements? But we can treat Requirements as qualifications. However we need both sections if content distinct. Could combine? The instruction: prefer a few substantive sections over many thin ones. So maybe we have About the role, Responsibilities, Requirements (including qualifications and skills), Benefits, Pay, Schedule. But they asked to use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can include those that have content. We have content for About the role, Responsibilities, Requirements (minimum experience), Qualifications (maybe degree etc.), Skills (Professional Skills Requirements), Benefits, Pay (salary ranges), Schedule (travel). So we can include all. We must not repeat job title as heading. Let's craft. First, About the role: combine We Are and You are paragraphs. We Are: "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: "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." Combine into prose. Responsibilities: The Work section bullet points. Let's list each sentence as list item. The Work sentences: - 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. We might separate travel into Schedule section later. Requirements: Here's What You Need bullet points. List: - 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) Qualifications: maybe same as Requirements? But we could treat Qualifications as degree requirement? However we already have degree in Requirements. Could split: Requirements = experience, Qualifications = education. Let's do that. Thus Requirements: experience bullets (first five). Qualifications: degree bullet. Skills: Professional Skills Requirements list. Let's extract each bullet: - 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. We'll need to split long sentences into list items? Each bullet is a skill; we can keep as
. Pay: Compensation paragraph plus salary ranges. Compensation paragraph: "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." Then list of location salary ranges. We'll need to present as list. Schedule: Travel may be required paragraph. Now we need to output HTML with headings
for each section.
We must not include any markdown fences.
Let's craft.
Structure:
About the role
...combined...
Responsibilities
- ... ...
Requirements
- ... ...
Qualifications
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
Skills
- ... ...
Benefits
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage