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Zora AI is Deloitte's AI agent platform delivering role\-/function\-specific products (e.g., Finance, Procurement, Supply Chain, Customer, Human Capital). As a Product Manager, you will own one or more sets of agent\-enabled products end\-to\-end\-defining vision, roadmap, requirements, and delivery\-while ensuring trust, adoption, and measurable business outcomes for enterprise users.
Key Responsibilities:
- Own product strategy and roadmap: Define product vision, target users, value propositions, and multi\-quarter roadmap across multiple role\-/function\-specific products.
- Translate needs into outcomes: Partner with clients/internal teams to identify high\-value use cases, map workflows, and define "jobs to be done" and measurable success metrics.
- Lead discovery and delivery: Run discovery (research, prototypes, pilots) and delivery (MVP to scale), managing scope, tradeoffs, and dependencies across engineering, data, and design.
- Define product requirements: Create PRDs, user stories, acceptance criteria, and workflow diagrams for agent behaviors, tool integrations, and user experiences.
- Agent experience \& orchestration: Specify agent capabilities (reasoning, task planning, tool use, approvals), human\-in\-the\-loop patterns, and escalation/exception handling.
- Data and integration leadership: Drive requirements for connectors, data access patterns, security/privacy, logging/auditability, and integration with enterprise systems.
- Trustworthy AI \& risk management: Partner with risk/compliance to address model governance, safety, monitoring, explainability, bias, and audit requirements.
- Go\-to\-market and enablement: Collaborate with sales and delivery to package offerings, define pricing/packaging inputs, create demos, and support pursuits and launches.
- Operate the product cadence: Maintain backlog, run sprint planning, track progress, and align stakeholders through clear decision points and communications.
Required Qualifications:
- 7\+ years of Product Management experience (enterprise software, SaaS, platforms, or data products), including shipping products from concept to GA.
- 2\+ years of recent experience delivering products involving AI/ML (GenAI preferred), including evaluation, monitoring, and iteration loops.
- 2\+ years of recent experience supporting product discovery (research, hypothesis testing, experimentation) and product delivery (requirements, backlog, release management).
- 1\+ year working with enterprise integration patterns (APIs, eventing, identity/SSO, role\-based access control, data pipelines).
- Limited immigration sponsorship may be available
- Ability to travel 0\-10%, on average, based on the work you do and the clients and industries/sectors you serve
Preferred:
- 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.
- Domain depth in one or more target functions (e.g., Finance, Procurement, Supply Chain, HR, Customer Operations).
- Consulting, enterprise transformation, or platform product experience (shared services, reusable components, governance).
- Proven ability to manage multiple products with competing priorities and shared platform dependencies.
- Experience launching products with OCI / SAP / ERP / CRM ecosystems and connector marketplaces.
- Excellent stakeholder management and executive communication; able to write crisp narratives, PRDs, and decision memos.
- Track record of partnering with engineering, design, data science, and risk/compliance teams to deliver in regulated or high\-stakes environments.
Key Deliverables
- Product strategy and 12\-18 month roadmap with measurable outcomes.
- PRDs, epics, user stories, and acceptance criteria for each product/agent capability.
- Use\-case catalog and prioritization model (value, feasibility, risk, readiness).
- MVP/pilot plans with success metrics, rollout phases, and scale criteria.
- Trust \& governance artifacts: evaluation approach, monitoring plan, audit/logging requirements, and risk controls (in partnership with risk teams).
- Release plans and launch readiness checklists (docs, training, demo scripts, enablement).
- Customer feedback loop: telemetry dashboards, VOC insights, and iteration plan.
How success will be measured (example outcomes)
- Adoption: active users, repeat usage, workflow completion rates, feature utilization by product set.
- Business impact: cycle\-time reduction for targeted workflows, cost\-to\-serve reductions, improved forecast accuracy or exception resolution time (by use case).
- Quality \& reliability: task success rate, low rework/rollback rates, latency/uptime targets, incident trends.
- Trust \& compliance: audit readiness, policy adherence, reduction in high\-severity model risks, successful governance reviews.
- Delivery excellence: roadmap predictability, on\-time releases, stakeholder satisfaction, reduced dependency blockers.
- Customer outcomes: pilot\-to\-scale conversion, referenceable wins, renewal/expansion influence (where applicable).
Working model \& stakeholders
- Working model: Remote \+ Hybrid (2\-3 days onsite) with flexibility based on team and client needs; operates in agile product teams with regular release cadence.
- 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 (Finance, Procurement, Supply Chain, HR, etc.)
+ Sales, Alliances, and Delivery/Implementation (pursuits, packaging, rollout)
+ Customer/Client stakeholders (product owners, process owners, IT, operations)
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 $113100 \- $232300\.
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.
\#EA\_ExpHire
Salary Context
This $113K-$232K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($172K) sits 5% below the category median. Disclosed range: $113K to $232K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Deloitte AI Hiring
Deloitte has 77 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Research Engineer. Positions span Stamford, CT, US, Austin, TX, US, Jersey City, NJ, US. Compensation range: $121K - $372K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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