Interested in this AI/ML Engineer role at Deloitte?
Apply Now →About This Role
As a Senior Product Owner within Deloitte's Converge for Health Care portfolio, you will play a key role in executing and advancing healthcare analytics, data, and AI\-enabled products. These capabilities are designed to help healthcare organizations improve performance, enable better decision\-making, and drive meaningful outcomes across clinical, financial, and operational domains.
In this role, you will work closely with a Product Manager to translate business priorities into executable product requirements, coordinate delivery with engineering and data teams, and ensure product capabilities align with real\-world client and practitioner needs. You will operate with a high degree of ownership at the intersection of healthcare domain expertise, technology platforms, and product execution\-bringing structure, clarity, and momentum to complex initiatives.
This position is well suited for practitioners who enjoy problem\-solving in ambiguous environments, cross\-functional collaboration, and building product capabilities that deliver tangible impact at scale.
Recruiting for this role ends on 08/01/2026\.
Work you'll do
As a Health Care Analytics and AI Senior Product Owner on Converge for Health Care's product management team, you will be responsible for:
- Driving execution of product roadmaps and delivery plans alongside the Product Manager, helping maintain alignment, momentum, and clarity as priorities move from concept through delivery.
- Translating business objectives, practitioner input, and client needs into clear user stories, functional requirements, and acceptance criteria, using a combination of healthcare domain knowledge and technical fluency to bridge business context with engineering and data teams.
- Partnering closely with engineering, data, and analytics teams to coordinate development, testing, and release activities, ensuring requirements are well understood and solutions align with intended use cases and outcomes.
- Managing day\-to\-day backlog operations, including prioritization, grooming, and sprint planning, while balancing near\-term delivery needs with longer\-term product direction.
- Participating in agile ceremonies and working sessions, helping ensure development progress aligns with roadmap priorities, delivery milestones, and stakeholder expectations.
- Contributing meaningfully to research, documentation, and design of new features and enhancements by engaging with Deloitte leaders, practitioners, and clients to gather insights, validate assumptions, and inform product decisions grounded in real\-world use.
- Supporting pilot efforts and early deployments of new analytics capabilities, including GenAI\-enabled features and emerging agentic workflows, and helping translate early learnings into scalable product improvements.
- Developing product collateral, demonstrations, and internal enablement materials to support adoption across Deloitte Consulting teams and client\-facing engagements.
Monitoring product usage, performance signals, and feedback, identifying opportunities for refinement, optimization, or future investment and surfacing these insights to the Product Manager and broader product team.
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A successful candidate would possess these skills:
- Demonstrated ability to translate ambiguous business and domain needs into structured product requirements and executable work items.
- Strong healthcare domain understanding, with experience applying analytics\- or data\-enabled solutions across healthcare operations and performance improvement contexts.
- Technical fluency with analytics platforms and data\-driven products, enabling effective collaboration with engineering, data, and analytics teams without requiring hands\-on coding.
- Strong problem\-solving and analytical skills, including the ability to synthesize inputs, evaluate tradeoffs, and articulate a clear "so what."
- Clear, effective written and verbal communication skills, with the ability to engage Product Managers, engineers, consultants, and senior stakeholders.
- Ability to influence outcomes and drive progress across cross\-functional teams operating in fast\-moving, matrixed environments.
- Comfort operating within agile delivery environments, including practical use of product and delivery management tools (e.g., backlog tracking, sprint planning, documentation, and collaboration tools) to organize work, communicate progress, and support effective execution.
- Curiosity about emerging technologies such as advanced analytics, automation, GenAI, and agentic systems, and interest in how these capabilities can be responsibly applied within healthcare products.
Comfort operating in evolving product portfolios where priorities, capabilities, and investment focus continue to mature over time.
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The team
This role sits within Converge for Health Care, Deloitte's industry\-focused asset studio for healthcare, and is part of Deloitte Consulting's Innovation \& Delivery Transformation (I\&DT) practice. I\&DT applies an engineering\- and innovation\-led mindset to how Deloitte builds, delivers, and scales technology\-enabled solutions.
Product Owners in Converge for Health Care work closely with Product Managers, engineering and data teams, Deloitte Consulting practitioners, and client stakeholders. The team operates at the intersection of healthcare domain expertise, analytics platforms, and delivery execution\-ensuring products are built with real\-world use in mind and continuously refined based on delivery and market feedback.
Qualifications
Required:
- Bachelor's degree in business, healthcare administration, information systems, computer science, or a related field
- 4\+ years of experience in healthcare consulting, product management, or technology delivery roles within the healthcare domain
- Ability to travel up to 20%, on average, based on client and project needs
- 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:
Master's degree in business administration, healthcare administration, health informatics, or a related field
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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: $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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($172K) sits 5% below the category median. Disclosed range: $116K to $229K.
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
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above 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 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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