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About This Role
About Accenture Song
Accenture Song accelerates growth and value for our clients by combining creativity, technology, and data-driven intelligence. Within Song, the Customer Technology – Commerce practice helps organizations design, build, and scale modern digital commerce ecosystems that deliver personalized, seamless, and intelligent customer experiences.
As AI agents increasingly mediate how consumers discover, evaluate, and purchase products, the quality and structure of product data has become the single most critical lever in commerce. We help brands transform their product data foundations — from fragmented master data and legacy PIM systems to AI-ready, structured product content that agents can find, trust, and act on across every channel.
WHAT'S IN IT FOR YOU?
- Build and lead Accenture Song's product data capability for Agentic Commerce — defining the methodology, shaping the offering, and setting the standard of excellence for the practice.
- Own C-suite advisory relationships with Chief Digital Officers, Chief Technology Officers, and VPs of eCommerce at leading organizations.
- Shape how Accenture goes to market on product data for AI commerce — from offer design and pricing to industry point of view and thought leadership.
- Drive commercial growth by identifying and converting new opportunities, leading proposals, and expanding existing client relationships.
- Build and develop a high-performing team of product data specialists, mentoring L7 Managers and elevating the practice capability across the organization.
- Establish Accenture as the recognized authority on product data for Agentic Commerce — through publications, speaking engagements, and ecosystem partnerships.
What You Would Do in This Role
As a Product Data & Agentic Commerce Senior Manager, you will help define and execute the product data capability within Accenture Song's Agentic Commerce practice. You will help define the methodology, lead the most complex and strategic client engagements, drive commercial growth, and build the team and IP that position Accenture as the leading authority on AI-ready product data.
Capability Leadership & Product Data Strategy
- Help to continuously evolve Accenture Song's product data readiness methodology for Agentic Commerce — including the audit framework, enrichment standards, governance model, and measurement approach — ensuring it is deployable, scalable, and differentiated in the market.
- Serve as an executive authority on product data strategy in client engagements — advising CDOs, CTOs, and C-suite stakeholders on the data investments required to compete in AI-mediated commerce and translating strategy into board-ready business cases.
- Provide Accenture's external point of view on the product data value chain for AI commerce — shaping how the market understands the relationship between data quality, LLM discoverability, protocol readiness, and commercial outcomes.
- Oversee multiple simultaneous client engagements at the strategic and advisory level, providing senior direction to L7 delivery teams executing product data transformation programs.
- Help maintain Accenture's product data IP and accelerators — includng the audit toolkits, enrichment frameworks, and readiness assessment tools that enable faster, more consistent delivery across the practice.
PXM, Product Content & Digital Shelf Excellence
- Help define Accenture Song's standard of excellence for PXM-led Agentic Commerce engagements — including the platform evaluation methodology, implementation approach, and quality bar for AI-ready product data delivery.
- Author and maintain internal methodology and reusable IP for AI-ready product data enrichment at scale — translating hard-won delivery experience into repeatable accelerators that shorten time to value.
- Build and maintain senior ecosystem relationships with key PXM platform vendors — including Salsify, Akeneo, and Syndigo — positioning Accenture as the preferred implementation and advisory partner in agentic commerce contexts.
- Advise clients on PXM platform selection and architecture decisions with the depth to challenge vendor recommendations and protect the client's long-term flexibility as agentic commerce requirements evolve.
- Set the technical and commercial quality bar for product attribute schema design, enrichment program scale, and multi-channel syndication — reviewing and approving the approaches developed by L7 delivery teams.
GEO Data Enablement & Protocol Readiness
- Help define Accenture's product data methodology for Generative Engine Optimization (GEO) engagements — defining how product content must be structured, verified, and distributed to maximize LLM citation rates, share of voice, and agentic recommendation confidence for brands.
- Lead Accenture engagements with the emerging agentic commerce protocol ecosystem (MCP, ACP, UCP) from a product data perspective — synthesizing protocol requirements into actionable data standards and contributing to Accenture's protocol readiness IP.
- Represent Accenture's product data point of view at industry forums, client leadership events, and ecosystem partnerships — establishing the practice's authority in GEO and AI-ready product data.
- Advise clients on the multi-year product data investment sequence required to move from GEO-readiness to full agentic commerce protocol compliance, helping leadership connect near-term data investments to long-term competitive positioning.
- Help build and maintain Accenture's perspective on regulatory and brand safety risk in AI-mediated product content — particularly in regulated categories — and ensure delivery teams consistently apply appropriate governance standards.
Enterprise Governance, Quality & Risk
- Define enterprise-level product data governance frameworks for clients — designing the organizational structures, ownership models, and operating cadences that sustain AI-ready product data quality at scale beyond the engagement.
- Advise executive committees and board-level stakeholders on the regulatory and reputational risk exposure created by AI-mediated product claims — particularly in food, beverage, personal care, and health categories — and define the governance guardrails that mitigate that risk.
- Establish the product data measurement and attribution standards used across the practice — defining how AI citation rate, share of voice in LLM responses, attribute completeness, and agentic transactability are measured, reported, and improved.
- Ensure delivery teams consistently apply responsible data management practices across all engagements — governing the accuracy of ingredient claims, allergen data, health assertions, and regulatory compliance in AI-facing product content.
Commercial Growth & Practice Development
- Drive revenue and pipeline growth for the product data capability — identifying new opportunities, qualifying prospects, leading executive discovery conversations, and converting pipeline into sold work.
- Lead or co-lead client proposals, RFP responses, and executive presentations for product data-focused Agentic Commerce opportunities — owning the commercial framing, value case, and solution design sections.
- Develop and steward senior executive relationships with key clients — serving as the trusted advisor and executive sponsor who identifies expansion opportunities and ensures client satisfaction at the C-suite level.
- Build, hire, and develop the product data specialist team within Accenture Song's Agentic Commerce practice — including direct management and career development of L7 Managers and recruitment of external talent.
- Define and maintain the practice's talent model for product data roles — establishing the skills, career paths, and assessment standards used to hire and develop the team.
- Contribute to Accenture's external thought leadership — publishing perspectives on AI-ready product data, speaking at industry events, and representing the practice in analyst and media conversations.
Basic Qualifications:
- 10+ years of progressive experience in product content management, digital commerce, or customer technology — with demonstrated seniority in product data strategy, practice or capability leadership, and executive-level advisory.
- 7+ years of deep, authoritative expertise in PXM and PIM platforms — specifically Salsify, Akeneo, or Syndigo — including the ability to evaluate platform fit, challenge vendor recommendations, design enterprise-scale implementations, and develop methodology based on delivery experience across multiple clients.
- 4+ years of comprehensive mastery of the end-to-end product data value chain: from R&D formulation and master data systems (SAP, Salesforce) through PXM enrichment, digital shelf management, and multi-channel syndication to AI-ready product feeds — including the ability to define and document this chain as reusable IP.
Preferred Qualifications:
- Substantive industry expertise — deep enough to advise a CDO on category-level data strategy, understand the commercial implications of SKU portfolio complexity, and translate product data investment into P&L terms that resonate at board level.
- Strong, current knowledge of AI and LLM discoverability requirements — including structured data standards, schema markup, machine-readable product attributes, citation signal dynamics, and GEO methodology — sufficient to define Accenture's external point of view on these topics.
- Working command of the agentic commerce protocol landscape (MCP, ACP, UCP) from a data and strategy perspective — including the ability to advise clients on protocol readiness investment and contribute to Accenture's protocol IP.
- Demonstrated track record of building or scaling a consulting capability, practice, or center of excellence — including methodology development, IP creation, talent development, and go-to-market positioning.
- Commercial experience in a consulting or advisory context: business development, proposal leadership, account growth, pipeline ownership, and the ability to shape and close senior client opportunities.
- Executive presence and C-suite communication skills — proven ability to advise CDOs, CTOs, CMOs, and board members on strategic data and technology investments, with the credibility and authority that comes from deep domain expertise.
- Experience building, managing, and developing high-performing teams — including the direct management of senior consultants and managers, and a track record of developing talent into more senior roles.
- External market presence commensurate with the seniority of the role: prior speaking engagements, published perspectives, analyst relationships, or ecosystem partnerships that reflect recognized expertise in product data or digital commerce.
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 10/30/2026.
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 $338,300
Colorado $132,500 to $292,200
Connecticut $132,500 to $292,200
District of Columbia $141,100 to $311,200
Illinois $122,700 to $292,200
Maine $112,900 to $249,000
Maryland $132,500 to $292,200
Massachusetts $132,500 to $311,200
Minnesota $132,500 to $292,200
New York $122,700 to $338,300
New Jersey $141,100 to $338,300
Ohio $122,700 to $270,600
Virginia $122,700 to $311,200
Washington $141,100 to $311,200
Requesting an Accommodation
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Salary Context
This $122K-$338K 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($230K) sits 9% above the category median. Disclosed range: $122K to $338K.
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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