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About This Role
Contractor Opportunity: AI-Native Forward Deployed Engineer
Location: Onsite – Sacramento, CA
Engagement Type: Contractor via Third-Party Supplier
Contracting Entity: Accenture
Supplier of Record: Manpower Group
Start Date: 10/29/2026
Duration: 6 Months with potential for extension depending on progress delivery
About the Role
We are seeking an experienced AI-Native Forward Deployed Engineer to design, build, and deploy enterprise-grade AI solutions that solve complex business challenges and deliver measurable operational impact. This is a highly technical, hands-on role for an engineer who thrives in ambiguous environments, works directly with stakeholders, and rapidly transforms business requirements into secure, scalable, production-ready AI applications.
As a key member of the delivery team, you will develop AI-native applications, agentic workflows, and cloud-native services that integrate seamlessly with enterprise platforms, data sources, and business processes. You will own the full implementation lifecycle, from discovery and prototyping through deployment, optimization, and operational support, while helping establish reusable AI engineering patterns and modern delivery practices.
This opportunity is open to individuals located in the Sacramento, CA and surrounding areas,
The project hours of operations will be Monday – Friday
Contractors will be engaged and paid by a third-party payroll supplier, Manpower Group, which will serve as the employer of record for tax and compliance purposes.
Key Responsibilities
- Partner directly with business stakeholders, architects, product teams, and engineers to identify and implement high-value AI transformation opportunities.
- Design, develop, and deploy AI-native applications, agentic workflows, and enterprise-grade AI solutions that improve business operations and user experiences.
- Translate complex business processes and operational challenges into scalable technical architectures and production-ready software solutions.
- Build secure, observable, and reliable AI systems utilizing retrieval-augmented generation (RAG), workflow orchestration, tool/function calling, human-in-the-loop processes, and policy-based decisioning.
- Develop cloud-native applications, APIs, integrations, and microservices that support AI-powered business capabilities.
- Drive implementation excellence by balancing performance, maintainability, security, scalability, and business value throughout the delivery lifecycle.
Deliverables or Project Scope
- Design and deploy production-quality AI applications and agent-based solutions that integrate with enterprise platforms, APIs, workflows, and data ecosystems.
- Implement enterprise AI capabilities including retrieval systems, prompt orchestration, model routing, reasoning workflows, structured outputs, and workflow automation.
- Build scalable backend services using cloud-native architectures, containers, Kubernetes, serverless technologies, event-driven processing, and infrastructure-as-code practices.
- Develop evaluation frameworks and monitoring solutions to improve AI accuracy, latency, reliability, cost efficiency, observability, and user adoption.
- Create reusable engineering assets, accelerators, design patterns, and implementation frameworks that accelerate future AI-native delivery initiatives.
- Modernize legacy systems, software delivery processes, and development workflows through practical application of GenAI, automation, and AI-assisted engineering techniques.
Collaboration Tools or Platforms
- Microsoft Office (Excel, Word, Outlook, Teams)
- AI/ML Platforms and Model Providers (commercial and open-source LLM platforms)
- Microsoft Azure, AWS, and/or Google Cloud Platform
- Kubernetes, Docker, and managed container platforms
- CI/CD and DevOps platforms (GitHub, GitLab, Azure DevOps, Jenkins, etc.)
- Workflow orchestration and automation platforms
- APIs, enterprise integration platforms, message queues, and event-streaming technologies
- Microsoft Office Suite, Microsoft Teams, and collaboration platforms
- Monitoring, logging, observability, and AI evaluation platforms
Qualifications
- High School Diploma or GED equivalent
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field.
- Minimum 8 years of professional software engineering experience developing enterprise-grade applications and distributed systems.
- Minimum 3 years of hands-on experience designing, deploying, or supporting AI, LLM, GenAI, or intelligent automation solutions in production or production-like environments.
- Experience developing backend applications using at least one modern programming language such as Python, Java, TypeScript/Node.js, Go, or C#.
- Demonstrated experience building APIs, microservices, integrations, and cloud-native applications.
- Hands-on experience with one or more of the following: RAG solutions, agentic workflows, LLM orchestration, tool/function calling, AI-assisted business workflows, or AI-native application development.
- Experience with containerization, CI/CD pipelines, automated testing, monitoring, and production operations.
- Knowledge of cloud-native architecture patterns including Kubernetes, serverless computing, event-driven systems, and infrastructure as code.
- Experience working within enterprise security, data governance, identity management, and access control requirements.
- Strong written and verbal communication skills with the ability to engage directly with technical and non-technical stakeholders
Preferred Qualifications
- 10+ years of enterprise software engineering or technical delivery experience.
- Previous experience as a Forward Deployed Engineer, Solutions Engineer, Platform Engineer, Technical Lead, or Senior Delivery Engineer.
- Experience building AI-native applications that support critical business workflows in production environments.
- Expertise integrating with enterprise AI platforms, model-serving environments, and multi-model architectures.
- Experience designing reusable AI engineering frameworks, platform abstractions, or developer enablement capabilities.
- Familiarity with AI evaluation frameworks, prompt/version management, model observability, tracing, and production AI monitoring.
- Experience supporting highly regulated, security-sensitive, or compliance-driven enterprise environments.
- Experience modernizing legacy systems and enterprise applications using cloud-native and AI-enabled approaches.
- Knowledge of workflow orchestration, policy-based routing, event-driven architecture, and asynchronous processing patterns.
- Experience optimizing AI workloads for quality, performance, cost, scalability, and operational reliability.
Additional Information / Requirements
- This is a 100% hands-on engineering role focused on designing, building, deploying, and supporting production-grade AI solutions.
- Candidates must be comfortable operating in ambiguous environments and rapidly converting evolving requirements into working software.
- Strong emphasis on implementation ownership, technical execution, production readiness, and delivery outcomes.
- Ability to collaborate effectively across engineering, architecture, product, business, and client-facing teams.
- Expected to document technical designs, implementation decisions, operational procedures, and best practices.
- Contribute to AI-native engineering standards, reusable assets, accelerators, and continuous improvement initiatives.
How to Apply
Please register and submit your resume at Avature Contractor Exchange. Qualified candidates will be contacted by Accenture’s Direct Sourcing team for initial screening, then by Manpower Group for next steps.
Compliance Notes
This is a contractor role administered through a third-party payroll supplier. The supplier will determine compensation, benefits, and applicable employment policies. The contractor will not be eligible for employee benefits from Accenture.
The supplier will ensure compliance with applicable federal and state laws including ADA, FMLA, and local ordinances
Contractors must have work authorization that does not now or in the future require sponsorship of a visa for work authorization in the United States.
Individuals currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.
Contractor candidates will not be obligated to disclose sealed or expunged records of conviction or arrest. A criminal conviction history is not an absolute bar to engagement with Accenture.
Accenture will not discriminate against individuals because they have inquired about, discussed, or disclosed their own pay or the pay of another individual. Those with access to compensation information as a part of their job functions may not disclose such information unless the disclosure is:
- In response to a formal complaint or charge,
- In furtherance of an investigation, proceeding, hearing, or action, or
- Consistent with Accenture’s legal duty to furnish information
California residents or those planning to work from Los Angeles County should review additional notifications here.
Please read Accenture’s Recruiting and Hiring Statement for more information on how we process your data during the contractor engagement process.
Compensation Disclosure
Compensation for contractor roles supporting Accenture is determined by the contractor’s employer Manpower Group, and varies depending on factors such as location, role, skill set, and experience.
As required by law, below you’ll find the reasonable range of compensation Manpower Group expects to pay for this role as set forth below.
We anticipate this contractor role will be posted until 10/22/2026.
Manpower Group offers a market competitive suite of benefits that may include medical, prescription drug, dental, and vision coverage; Health Savings Accounts (HSA) and Flexible Spending Accounts (FSA); 401(k) retirement savings plans; supplemental life insurance; short-term and long-term disability insurance; business travel insurance; employee assistance and wellness programs; commuter benefits; and additional voluntary benefits such as identity protection, legal services, and critical illness, accident, and hospital indemnity insurance.
Role Location
Hourly Range
California
$91.00 to $120.00
Colorado
$82.00 to $106.00
Connecticut
$84.00 to $108.00
District of Columbia
$89.00 to $115.00
Illinois
$84.00 to $111.00
Maine
$79.00 to $101.00
Maryland
$87.00 to $113.00
Massachusetts
$91.00 to $118.00
Minnesota
$82.00 to $106.00
New Jersey
$89.00 to $115.00
New York
$94.00 to $123.00
Ohio
$77.00 to $103.00
Virginia
$84.00 to $108.00
Washington
$91.00 to $120.00
Drug-Free Workplace
Accenture maintains a drug-free workplace. Engaging in illegal activities, including drug abuse, may result in termination or other appropriate action. The unlawful manufacture, sale, distribution, transfer, dispensation, possession, or use of controlled substances or marijuana in the workplace is strictly prohibited.
Equal Employment Opportunity Statement
Accenture believes that no one should be discriminated against because of their differences. All decisions regarding contractor engagement 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 protected by applicable law.
Accenture’s commitment to diversity and inclusion extends to all individuals performing services on its behalf, including those engaged through third-party suppliers. For details, view a copy of the Accenture Equal Opportunity Statement.
Accenture is an EEO and Affirmative Action supporter of Veterans and Individuals with Disabilities and is committed to providing veteran engagement opportunities.
Accenture is committed to fostering an inclusive environment and ensuring equal opportunity for individuals with disabilities or religious observances. If you are hired to perform services as a contractor for Accenture, you may request reasonable accommodations through the supplier, which will coordinate with Accenture as needed to support your ability to perform essential job functions.
Salary Context
This $174K-$255K range is above the 75th percentile 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, 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. Disclosed range: $174K to $255K.
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 AI Hiring
Accenture has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, Prompt Engineer. Positions span New York, NY, Mountain View, CA, US, Morristown, NJ, US. Compensation range: $218K - $434K.
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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