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About This Role
Posted Date 7/19/2026 Description 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?
- Lead the product data foundation that powers Agentic Commerce transformations for leading brands.
- Work at the intersection of product experience management, AI discoverability, and digital commerce — where data quality directly determines commercial outcomes.
- Define how brands structure, enrich, and syndicate product content so AI agents can find, trust, and transact on it reliably.
- Shape Accenture's product data readiness frameworks, GEO data offerings, and AI-ready PXM accelerators.
- Partner with GEO specialists, commerce architects, and client commercial teams to build the data foundation every agentic capability depends on.
What You Would Do in This Role
As a Product Data & Agentic Commerce Manager, you will lead the product data strategy and implementation workstream within Agentic Commerce transformation engagements. You will be the bridge between what AI agents require to surface and transact on products, and what clients need to do commercially and operationally to get there.
Product Data Strategy & AI Readiness
- Lead product data readiness assessments for clients — diagnosing the gaps between current data states and the structured, machine-readable content that AI agents require to discover, recommend, and transact on products.
- Define and implement product data value chain strategies: from R&D and master data systems (SAP, Salesforce) through PXM enrichment and digital shelf management to AI-ready product feeds consumed by LLMs and agentic commerce protocols.
- Establish unified, structured product data foundations — ensuring attribute completeness, data quality, and content consistency across master data, PXM platforms, and channel-specific feed outputs.
- Translate AI discoverability requirements — LLM citation signals, agentic recommendation ranking factors, protocol data specifications — into concrete product data architectures and prioritized enrichment roadmaps.
- Partner with client Chief Digital Officers, VPs of eCommerce, and category management teams to align product data strategy with commercial priorities and measurable P&L outcomes.
PXM, Product Content & Digital Shelf Excellence
- Lead PXM strategy and platform implementation across Salsify, Akeneo, and Syndigo — guiding clients from PIM-only data management to AI-ready PXM capability built for agentic commerce surfaces.
- Define product attribute schemas and content standards that serve both human-facing commerce surfaces and machine-readable AI requirements — including ingredient claims, usage occasions, consumer benefit attributes, and structured comparison data.
- Design and oversee AI-assisted product content enrichment programs — structuring and scaling enrichment across portfolios of hundreds to thousands of SKUs at commercially relevant pace.
- Architect product feed designs that support multi-channel syndication across retailer portals, marketplace platforms, LLM discovery surfaces, and emerging agentic commerce protocol integrations.
- Establish data completeness and quality measurement frameworks that directly connect product attribute quality to AI discoverability outcomes and share of voice in AI-generated answers.
GEO Data Enablement & Protocol Readiness
- Drive the product data component of Generative Engine Optimization (GEO) programs — ensuring product content is structured, verified, and authoritative enough for LLMs to confidently cite, recommend, and surface brands in AI-generated answers.
- Lead assessments of product feed readiness for emerging agentic commerce protocols (MCP, ACP, UCP) — translating protocol data requirements into actionable specifications that client technology and content teams can execute against.
- Define and implement schema markup strategies — product schema, ingredient and claims schema, review schema — that increase brand visibility and citation confidence across AI-driven discovery surfaces.
- Audit and improve third-party product content ecosystems (retailer portals, syndication networks, review platforms) to strengthen a brand's AI citation footprint beyond its owned channels.
- Advise clients on sequencing product data investment across the Now / Near / Next Agentic Commerce journey — matching data readiness investments to protocol maturity and LLM platform adoption curves.
Data Governance, Quality & Brand Safety
- Define product data governance frameworks that manage attribute completeness, content freshness, and accuracy across all AI-facing channels.
- Establish product data quality KPIs and measurement systems: tracking attribute completeness scores, AI citation rates, share of voice in LLM responses, and product-level recommendation accuracy as direct outcomes of data quality investment.
- Build and govern structured product data pipelines that ensure consistency and traceability from source systems (R&D, master data) through to the agent-facing surface.
- Implement responsible data management practices ensuring accuracy of ingredient claims, allergen data, health and beauty assertions, and regulatory compliance in AI-mediated product recommendations — understanding that an AI agent surfacing incorrect product claims carries both commercial and regulatory risk.
Consulting & Commercial Leadership
- Engage senior client stakeholders — CDOs, VPs of eCommerce, CMOs, and category directors — to articulate why product data is the foundational investment that determines Agentic Commerce success or failure, and translate that into an actionable business case.
- Develop executive presentations, value cases, and roadmaps that connect product data quality gaps to commercial risk, AI visibility loss, and competitive displacement — framing data investment in terms that drive CFO and board-level action.
- Lead the product data workstream within end-to-end Agentic Commerce programs, managing timelines, resources, stakeholder alignment, and client capability building across the engagement lifecycle.
- Mentor consultants and analysts on product data enrichment, PXM platforms, AI discoverability diagnostics, and GEO data practices — building durable team capability in a fast-evolving discipline.
- Contribute to Accenture's Agentic Commerce product data accelerators, GEO frameworks, AI-readiness audit toolkits, and go-to-market offerings.
Basic Qualifications:
- 6+ years of experience in product content management, digital commerce, or customer technology — with deep, hands-on expertise in product data strategy, enrichment, and governance at scale.
- 5+ years of hands-on platform experience with PXM and PIM systems — specifically Salsify, Akeneo, or Syndigo — including data modeling, enrichment workflow design, attribute schema definition, and multi-channel syndication configuration.
Preferred Qualifications:
- Strong working understanding of the end-to-end product data value chain: from R&D formulation and master data systems (SAP, Salesforce) through PXM enrichment and digital shelf management to AI-ready product feeds.
- Substantive industry experience — genuine understanding of category management, SKU portfolio complexity, multi-retailer data requirements, and how product data quality translates into commercial shelf and search outcomes.
- Working familiarity with AI and LLM discoverability requirements: structured product attributes, schema markup standards, machine-readable content formats, and citation signal dynamics — and how these differ from traditional SEO in what they demand from product data.
- Practical knowledge of agentic commerce protocols (MCP, ACP, UCP) from a data readiness perspective — understanding what structured product data must look like to be queryable and transactable by AI agents, without requiring hands-on protocol engineering.
- Experience leading product data quality programs: defining attribute completeness standards, building data quality measurement frameworks, and managing large-scale enrichment programs across complex SKU portfolios.
- Proven ability to translate product data quality gaps into commercial and strategic terms for client leadership — connecting data deficiencies to revenue risk, AI visibility gaps, and competitive displacement.
- Strong consulting skills: executive communication, structured storytelling, value case development, stakeholder management, and end-to-end program leadership across complex multi-stakeholder engagements.
- Experience managing cross-functional delivery teams spanning commerce, marketing, R&D, and technology — with the organizational fluency to align stakeholders who don't naturally share a common data language.
- Comfort operating in a fast-moving, ambiguous space where standards — protocols, measurement frameworks, AI platform requirements — are still forming, and the ability to make sound recommendations while those standards evolve.
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 $94,400 to $293,800
Colorado $94,400 to $253,800
Connecticut $94,400 to $253,800
District of Columbia $100,500 to $270,300
Illinois $87,400 to $253,800
Maine $80,400 to $216,200
Maryland $94,400 to $253,800
Massachusetts $94,400 to $270,300
Minnesota $94,400 to $253,800
New York $87,400 to $293,800
New Jersey $100,500 to $293,800
Ohio $87,400 to $235,000
Virginia $87,400 to $270,300
Washington $100,500 to $270,300
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 11/16/2026. 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 $94,400 to $293,800 Colorado $94,400 to $253,800 Connecticut $94,400 to $253,800 District of Columbia $100,500 to $270,300 Illinois $87,400 to $253,800 Maine $80,400 to $216,200 Maryland $94,400 to $253,800 Massachusetts $94,400 to $270,300 Minnesota $94,400 to $253,800 New York $87,400 to $293,800 New Jersey $100,500 to $293,800 Ohio $87,400 to $235,000 Virginia $87,400 to $270,300 Washington $100,500 to $270,300 Requesting an Accommodation
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Salary94,400.00 - 293,800.00 Annual Type Full-time
Salary Context
This $87K-$293K range is above the median 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 Information Technology Senior Management Forum, 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 ($190K) sits 10% below the category median. Disclosed range: $87K to $293K.
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.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 30 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, MLOps Engineer, AI Safety. Positions span New York, NY, US, McLean, VA, US, Riverwoods, IL, US. Compensation range: $168K - $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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