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About This Role
At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations.
Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more.
Join us to drive positive, lasting change that moves missions and the government forward!
As a Enterprise AI Business Analyst on this project, you will play a pivotal role in implementations for Google Gemini for Gov, M365 Copilot, and ChatGPT Gov Enterprise offerings within Accenture Federal Services. Your primary responsibilities will include collaborating closely with stakeholders to identify and document business requirements, as well as determining configuration needs specific to the organization's security objectives. You will be expected to facilitate workshops and lead user feedback collection. Your ability to translate complex business needs into actionable technical specifications will be essential. Experience working in small, agile teams and delivering results under tight deadlines will enable you to thrive in this dynamic environment. Your background in process development, requirements gathering, and stakeholder engagement will be key assets as you help shape new workflows.
Program Leadership & Governance
- Ensure alignment with AFS legal and security requirements and federal compliance standards (e.g., NIST 800-171).
Stakeholder Engagement
- Collaborate with stakeholders to identify and document business requirements
Functional Oversight
- Define business requirements and success metrics for platform deployment and adoption.
- Facilitate requirements workshops
- Lead user feedback collection and incorporate insights into continuous improvement plans.
- Align technical deliverables with business objectives and compliance mandates.
Operational Readiness
- Coordinate procurement activities and operational approvals in partnership with the operations team.
- Oversee integration of identity management (Entra ID SSO), logging, and monitoring capabilities.
- Ensure dedicated support resources are onboarded and trained for ongoing platform operations.
- Support testing, validation, and rollout
Risk & Issue Management
- Communicate project progress, risks, and issues to stakeholders and team members
- Identify and manage program risks, dependencies, and constraints across multiple workstreams.
- Develop contingency plans and escalate critical issues promptly.
Team Leadership
- Work closely with a small, fast-paced internal team to ensure timely and efficient project delivery
- Collaborate with the Enterprise AI Feature Engineer to ensure technical and functional workstreams remain synchronized.
What you need
- Bachelor's Degree Required
- US Citizenship required
- Experience with at least one of the following enterprise AI tools:
- Google Gemini for Gov
- M365 Copilot
- ChatGPT Gov
- Stakeholder Management
- Business Process Engineering
- Exceptional communication, stakeholder management, collaboration skills
- Proven experience managing complex technology integration programs in a federal or highly regulated environment.
- Strong understanding of AI platforms, cloud technologies, and compliance frameworks (e.g., FedRAMP, NIST).
- Ability to navigate ambiguity and drive results in fast-paced, evolving environments.
- Expertise in cloud-based infrastructure and DevOps practices
Bonus if you have
- Familiarity with LLMS and LLM Cloud Services
- Familiarity with Google Cloud Platform or other Cloud services
- Experience with supply chain systems and risk assessment
- Familiarity with cloud deployment environments
What We Believe**As a company wholly dedicated to serving the US federal government, we bring together the best talent to help reinvent how federal agencies operate and deliver greater value for their mission and the American people. We have an unwavering commitment to creating a culture in which all our people are respected, feel a sense of belonging, and have equal opportunity. As a business imperative, every person at Accenture Federal Services has the responsibility to create and sustain a culture where everyone feels welcomed and included. This is grounded in our core values and our experience that hiring and developing great people who reflect different perspectives, experiences, and backgrounds is key to driving innovation and delivering the results that our clients and the country count on.
Equal Employment Opportunity Statement We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities. For details, view a copy of the Accenture Federal Services Equal Opportunity Policy Statement.
Accenture Federal Services is an Equal Employment Opportunity employer. Additionally, as an Affirmative Action Employer for Veterans and Individuals with Disabilities, Accenture Federal Services is committed to providing veteran employment opportunities to our service men and women.
Requesting An Accommodation Accenture Federal Services is committed to providing equal employment opportunities for persons with disabilities or religious observances, including reasonable accommodation when needed. If you are hired by Accenture Federal Services and require accommodation to perform the essential functions of your role, you will be asked to participate in our reasonable accommodation process. Accommodations made to facilitate the recruiting process are not a guarantee of future or continued accommodations once hired.
If you**are being considered for employment opportunities with Accenture Federal Services and need an accommodation for a disability or religious observance during the interview process or for the job you are interviewing for, please speak with your recruiter.
Other Employment Statements Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
Candidates who are currently employed by a client of Accenture Federal Services or an affiliated Accenture business may not be eligible for consideration.
Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process.
The Company will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Additionally, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the Company's legal duty to furnish information.
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Salary Context
This $81K-$162K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Accenture Federal Services, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($121K) sits 43% below the category median. Disclosed range: $81K to $162K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Accenture Federal Services AI Hiring
Accenture Federal Services has 14 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, Data Engineer. Positions span Washington, DC, Chantilly, VA, Seattle, WA. Compensation range: $162K - $206K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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