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The Team
Zora AI™ by Deloitte is Deloitte's agentic AI platform, delivering role- and function-specific products - including Finance, Procurement, Supply Chain, Customer, and Human Capital - built on the NVIDIA AI stack and underpinned by Deloitte's Trustworthy AI™ framework.
Position Summary
As a Senior Consultant Product Manager focused on Sourcing & Procurement, you own the delivery of an agentic product area within the procurement portfolio end-to-end at the feature and workstream level. You contribute to and help shape the vision and roadmap set by the Product Lead, run discovery and delivery, and bring deep procurement understanding to every decision - all while ensuring trust, adoption, and measurable business outcomes for enterprise users. You'll operate hands-on inside an agile product team, coordinate closely across engineering, data science, design, and risk, and mentor Consultants and Analysts as you go. The role sits where procurement domain knowledge meets agentic AI, turning real practitioner pain points into digital workers that get work done.
Recruiting for this position ends on 10/31/26.
Work you'll do
Product ownership & value delivery
- Own the delivery of a defined sourcing and procurement product/agent area, and contribute to the product vision, strategy, and 12-18 month roadmap led by the Product Lead.
- Define and track KPIs tied to procurement efficiency, cost reduction, and supplier performance; use data to continuously refine direction and retire low-value work.
- Drive features that address end-to-end procurement workflows - requisition, sourcing and RFx, contract management, supplier onboarding and collaboration, and purchase-to-pay.
- Translate business needs into outcomes: identify high-value use cases, map workflows, and define "jobs to be done" with measurable success metrics.
Domain expertise & user engagement
- Develop and apply deep understanding of procurement processes (direct/indirect sourcing, RFx, spend analytics, supplier management, P2P) to credibly lead product decisions.
- Conduct user research and competitive analysis across procurement platforms to surface unmet needs and opportunities.
- Engage directly with procurement practitioners, category managers, and finance stakeholders to translate pain points into prioritized solutions.
Discovery & delivery
- Run discovery (research, prototypes, Alphas) and delivery (MVP to scale) for your product area, managing scope, tradeoffs, and dependencies across engineering, data, and design.
- Create clear PRDs, epics, user stories, acceptance criteria, and workflow diagrams for agent behaviors, tool integrations, and user experiences.
- Maintain a healthy, well-prioritized backlog; run sprint planning, track progress, remove blockers, and keep stakeholders aligned through clear decision points.
Agent experience & orchestration
- Specify agent capabilities (reasoning, task planning, tool use, approvals), human-in-the-loop patterns, and escalation/exception handling for procurement workflows.
- Partner with engineering and data teams on requirements for connectors, data access patterns, security/privacy, logging/auditability, and integration with enterprise procurement and ERP systems.
Trustworthy AI & risk
- Partner with risk, compliance, and cybersecurity teams to address model governance, safety, monitoring, explainability, bias, and audit requirements, aligned to the Trustworthy AI™ framework.
Go-to-market & enablement
- Collaborate with sales, alliances, and delivery to package offerings, create demos, and support pursuits, pilots, and launches; contribute inputs to pricing and packaging.
AI-augmented ways of working
- Use AI to accelerate domain research, synthesize supplier and spend-data insights, and rapidly prototype and validate procurement workflow improvements.
- Test assumptions early, retire low-value ideas fast, and focus the team on the solutions with the highest impact on procurement operations.
The successful candidate would possess these skills
Key deliverables
- Delivery of a defined sourcing & procurement product/agent area, with contributions to the broader product strategy and roadmap.
- PRDs, epics, user stories, and acceptance criteria for each product/agent capability in your area.
- Use-case catalog and prioritization inputs (value, feasibility, risk, readiness) for procurement workflows.
- MVP/pilot plans with success metrics, rollout phases, and scale criteria.
- Trust & governance artifacts: evaluation approach, monitoring plan, and audit/logging requirements (in partnership with risk teams).
- Release plans and launch-readiness inputs (docs, training, demo scripts, enablement).
- Customer feedback loop: telemetry dashboards, voice-of-customer insights, and an iteration plan.
How success will be measured (example outcomes)
- Adoption: active users, repeat usage, workflow completion rates, and feature utilization across the procurement product area.
- Business impact: cycle-time reduction for targeted procurement workflows, cost-to-serve and spend reductions, and improved supplier performance or exception-resolution time by use case.
- Quality & reliability: task success rate, low rework/rollback rates, latency/uptime targets met, and healthy incident trends.
- Trust & compliance: audit readiness, policy adherence, reduction in high-severity model risks, and successful governance reviews.
- Delivery excellence: roadmap predictability, on-time releases, stakeholder satisfaction, and reduced dependency blockers.
- Customer outcomes: pilot-to-scale conversion and referenceable wins within procurement engagements.
Working model & stakeholders
You'll work in a remote + hybrid model (typically 2-3 days onsite), with flexibility based on team and client needs, operating in agile product teams on a regular release cadence.
You'll also mentor Consultants and Analysts on the team, sharing product practices and helping raise delivery quality.
Core stakeholders:
- Engineering (platform + product squads)
- Data Science / Applied AI (models, evaluation, tuning)
- Design / Research (UX, workflow design, prototyping)
- Cybersecurity & Privacy (security controls, data protection)
- Risk, Legal & Compliance (AI governance, auditability, policy alignment)
- Domain SMEs - especially procurement, sourcing, supply chain, and finance
- Sales, Alliances & Delivery/Implementation (pursuits, packaging, rollout)
- Customer/client stakeholders - procurement leaders, category managers, process owners, IT, and operations
Qualifications
Required
- Bachelor's degree in a technical, analytical, or related field (e.g., engineering, computer science, data science, mathematics, or finance).
- 5-8 years of product management experience (enterprise software, SaaS, platforms, or data products), including shipping products or major features from concept to launch.
- Ability to own delivery of a product area with competing priorities and shared platform dependencies.
- Experience delivering products involving AI/ML (GenAI preferred), including evaluation, monitoring, and iteration loops.
- Solid command of product discovery (research, hypothesis testing, experimentation) and delivery (requirements, backlog, release management).
- Working knowledge of enterprise integration patterns (APIs, eventing, identity/SSO, role-based access control, data pipelines).
- Experience partnering with engineering, design, data science, and risk/compliance teams to deliver in regulated or high-stakes environments.
- Exposure to procurement, sourcing, or supply chain - either as a product focus or through direct stakeholder work - with the ability to build domain depth quickly.
- Strong stakeholder management and communication; able to write crisp narratives, PRDs, and decision memos.
- Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Preferred
- Domain depth in procurement/sourcing (direct/indirect sourcing, RFx, spend analytics, supplier management, purchase-to-pay).
- Experience with agentic architectures (tool calling, retrieval-augmented generation, workflow orchestration, multi-agent patterns).
- Familiarity with LLM evaluation (quality metrics, red-teaming, grounding, hallucination mitigation) and observability.
- Consulting, enterprise transformation, or platform product experience (shared services, reusable components, governance).
- Experience with OCI / SAP / ERP / CRM ecosystems, procurement suites, and connector marketplaces.
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $116,200-$229,100.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Salary Context
This $116K-$229K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Deloitte, 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 in Demand for This Role
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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($172K) sits 19% below the category median. Disclosed range: $116K to $229K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Deloitte AI Hiring
Deloitte has 109 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Agent Developer. Positions span Perth, Western Australia, Australia, New York, NY, Tulsa, OK. Compensation range: $179K - $278K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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