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About This Role
Forward Deployed Engineer Senior Manager | Senior Level | Full time Job No. R00357555 | Multiple Locations
We are:
Accenture Song accelerates growth and value for our clients through sustained customer relevance. Our capabilities span ideation to execution: growth, product and experience design; technology and experience platforms; creative, media and marketing strategy; and campaign, content and channel orchestration. With strong client relationships and deep industry expertise, we help our clients operate at the speed of life through the unlimited potential of imagination, technology and intelligence. Visit us at: www.accenture.com/song
About the Role
We are building out practitioners within our Song – Sales practice: AI Forward Deployed Engineers who embed directly with clients to design, prototype, and deploy Agentic AI solutions that transform how our clients sell, serve, and operate. You will partner closely with our Sales teams to win new business, land at client sites to uncover opportunity, and stay to build solutions that deliver measurable ROI.
What You’ll Do
- Conduct client-facing discovery to identify and prioritize AI use cases with the highest commercial impact
- Design Agentic AI architectures and build rapid prototypes to demonstrate value before full engagement
- Collaborate with Sales to develop business cases, executive presentations, and RFP responses
- Lead on-site AI workshops and co-creation sessions with client leadership and technical teams
- Own the technical solution end to end — from first meeting through prototype, production rollout, adoption, and operational handoff
- Integrate AI agents with client enterprise systems, data platforms, and workflows
- Harden solutions for production — testing, evals, observability, and cost/latency/safety trade-offs — so they run reliably after handoff
- Establish AI governance standards and ensure responsible deployment at client environments
- Mentor junior team members and contribute to internal AI capability building
What We’re Looking For (Basic Qualifications):
- 10+ years in a client-facing technical or engineering role with experience supporting Sales & Revenue Processes and Platforms.
- 3+ years of experience record building AI or LLM-powered applications in production. Candidates should have strong production engineering skills — Python and/or TypeScript, APIs and integrations, testing, and deployment — plus hands-on modern AI/ML tooling.
Preferred Qualifications:
- Ability to communicate across the spectrum from developer to C-suite
- A portfolio of real-world AI projects or deployed solutions
Why This Role
- You'll do work few people can — shipping production Agentic AI and owning the client relationship at the same time, a scarce hybrid skill set in high demand across AI labs, enterprise SaaS, and consulting
- High visibility, high impact — your work goes live at clients and drives measurable ROI, not into a backlog
- Direct exposure to frontier models and platforms through partnerships with Anthropic, OpenAI, Agentforce, and the major clouds
- Build reusable assets, accelerators, and playbooks that scale your impact across engagements — feeding a field-to-product learning loop that shapes what we build next
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 11/01/2026. 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
Role Location Annual Salary Range California $132,500 to $366,300 Colorado $132,500 to $316,400 Connecticut $132,500 to $316,400 District of Columbia $141,100 to $337,000 Illinois $122,700 to $316,400 Maine $112,900 to $269,600 Maryland $132,500 to $316,400 Massachusetts $132,500 to $337,000 Minnesota $132,500 to $316,400 New York $122,700 to $366,300 New Jersey $141,100 to $366,300 Ohio $122,700 to $293,000 Virginia $122,700 to $337,000 Washington $141,100 to $337,000
Chicago, IL
Albany, NY
Arlington, VA
Atlanta, GA
Austin, TX
Beaverton, OR
Bentonville, AR
Boston, MA
Carmel, IN
Charlotte, NC
Cincinnati, OH
Cleveland, OH
Columbus, OH
Culver City, CA
Denver, CO
Des Moines, IA
Detroit, MI
Hartford, CT
Houston, TX
Irvine, CA
Irving, TX
Kirkland, WA
Miami, FL
Milwaukee, WI
Minneapolis, MN
Morristown, NJ
Mountain View, CA
Nashville, TN
New York City, NY
Oklahoma City, OK
Orlando, FL
Overland Park, KS
Philadelphia, PA
Pittsburgh, PA
Raleigh, NC
Redmond, WA
Sacramento, CA
San Diego, CA
San Francisco, CA
Scottsdale, AZ
Seattle, WA
St. Louis, MO
St. Petersburg, FL
Walnut Creek, CA
Requesting an Accommodation
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Equal Employment Opportunity Statement
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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 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. Further, at Accenture a criminal conviction history is not an absolute bar to employment.
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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We work with one shared purpose: to deliver on the promise of technology and human ingenuity. Every day, more than 775,000 of us help our stakeholders continuously reinvent. Together, we drive positive change and deliver value to our clients, partners, shareholders, communities, and each other.
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Salary Context
This $122K-$337K 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 Logic, Inc., 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($229K) sits 8% above the category median. Disclosed range: $122K to $337K.
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.
Logic, Inc. AI Hiring
Logic, Inc. has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Chicago, IL, US, Seattle, WA, US, Mountain View, CA, US. Compensation range: $205K - $434K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national 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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