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About This Role
Overview: About the Role & Team
IDC is building an enterprise AI capability that moves beyond isolated pilots: a secure, reusable platform for deploying AI agents, connecting them to enterprise data and tools, and proving
measurable business value. We are looking for a high-ownership Senior Manager, AI Program Management to build the AI PM function from the ground up alongside the VP of AI. This person will bring structure, transparency, and execution discipline to a fast-moving portfolio of AI platform, data, governance, and business-facing agent initiatives.
This is not a traditional project-coordination role. You will help decide what gets built, turn ambiguous ideas into executable plans, establish the operating model for AI delivery, and keep cross-functional teams focused on outcomes. We need someone who does this work personally — drafting the charter, chasing the dependency, rewriting the deck before the steering meeting — not someone who assigns it and checks in later.
What You’ll Do
- Build IDC’s AI program-management function, including standards, rituals, templates, portfolio reporting, intake, prioritization, decision tracking, and delivery governance.
- Partner directly with the VP of AI to translate strategy into an actionable roadmap across AI platform, agent products, governance, observability, adoption, and business use cases.
- Turn loosely defined business opportunities into clear charters with problem statements, expected value, owners, scope, dependencies, success metrics, milestones, and definition of done.
- Create and operate a portfolio view of AI initiatives, making tradeoffs and capacity constraints visible to executive leadership.
- Lead end-to-end delivery of complex AI programs involving engineering, data, security, legal, enterprise architecture, product, and business stakeholders.
- Own intake and prioritization end-to-end — run the pragmatic process that decides what enters the portfolio, in what order, and distinguishes experiments from scalable production investments.
- Work directly with delivery teams to gather deliverables, status, and artifacts, following up yourself when something is late or off-track rather than waiting for teams to report up.
- Define a repeatable delivery lifecycle for AI products—from discovery and data readiness through pilot, evaluation, production launch, adoption, and ongoing measurement.
- Drive cross-functional alignment on requirements, risks, decisions, dependencies, and timelines; escalate early when delivery, security, data, or adoption risks threaten outcomes.
- Create executive-ready communication that clearly explains progress, business value, risks, decisions needed, and next steps.
- Help establish measurement standards for AI initiatives, including adoption, quality, reliability, cost, risk, and realized business value.
- Partner with technical leaders to ensure plans account for architecture, integrations, data access, evaluation, security, governance, operational support, and change management.
- Help build the operating cadence for the AI organization: weekly delivery reviews, monthly portfolio and value reviews, and executive steering updates.
- Mentor future program and project managers as the function grows.
Initial focus areas
In the first year, this role will help organize and deliver a portfolio that includes:
- Enterprise AI platform and shared AWS foundations for secure agent deployment
- MCP and enterprise-tool integration standards
- AI observability, evaluation, governance, and cost-management capabilities
- Agentic business solutions across sales, research/data collection, service operations, and internal
productivity
- AI adoption, training, change management, and business-value measurement
- Executive-facing visibility into the AI portfolio, operational health, and outcomes
What success looks like
First 90 days
- Establish a clear, trusted view of the AI portfolio, including active initiatives, ownership, dependencies, risks, investment, and expected outcomes.
- Implement a lightweight but consistent intake, prioritization, and program-management framework.
- Create working cadences for delivery teams and executive stakeholders.
- Bring the highest-priority initiatives to an agreed roadmap, with measurable objectives and clear
delivery plans.
- Identify initiatives that should be accelerated, paused, combined, or stopped.
Within 12 months
- AI work is managed as a coherent portfolio rather than a collection of disconnected requests.
- Leaders have reliable visibility into delivery progress, risks, investment, adoption, and business value.
- Teams can move from idea to pilot to production through a repeatable, well-governed process.
- AI initiatives have defined owners, success metrics, evaluation plans, operating models, and adoption plans.
- The AI organization has a scalable PM function that can support a growing enterprise portfolio.
What You Bring* A demonstrated bias toward doing the work yourself — writing the charter, building the tracker, chasing the blocker — rather than assigning it and following up in a status meeting.
- 8+ years of program, product, transformation, or portfolio-management experience, including ownership of complex cross-functional technology programs.
- Meaningful experience delivering AI, data, analytics, cloud-platform, automation, or enterprise-software initiatives. Direct generative AI or agentic AI experience is strongly preferred.
- Experience building or materially improving a PMO, program-management practice, product operations function, or portfolio-management model.
- Strong ability to convert ambiguity into structured, practical plans without creating unnecessary process.
- Experience working with technical teams and understanding the realities of data, integrations, cloud infrastructure, security, software delivery, and production operations.
- Strong executive presence and excellent written communication; able to frame decisions and tradeoffs clearly for both technical and non-technical leaders.
- A bias toward business outcomes: you know that a successful AI initiative must drive adoption and measurable value, not merely ship a model or feature.
- Comfort operating in a lean, evolving organization where priorities will change and you will need to create the structure as you go.
- Strong judgment about when to standardize, when to experiment, and when to stop work that is not producing value.
Preferred qualifications
- Experience leading enterprise AI, data, or digital-transformation portfolios.
- Experience in a consulting, research, technology, SaaS, professional-services, or data-products environment.
- Familiarity with AWS, enterprise data platforms, Salesforce, Microsoft 365, Jira/Confluence, and modern AI/agent architectures.
- Experience with AI governance, responsible AI, model evaluation, observability, security, or change management.
- PMP, PgMP, SAFe, Agile, product-management, or similar credentials are welcome but not required.
Working style
You are structured without being bureaucratic. You can operate at executive altitude and still get into the details when a team is blocked. You are comfortable challenging vague requests, surfacing difficult tradeoffs, and bringing clarity to a rapidly changing AI landscape. This is a hands-on role: you will personally drive plans, chase dependencies, and unblock work — not merely track status and report it upward. Most importantly, you are energized by building a new function and helping an organization turn AI ambition into real, measurable outcomes. Why This Role Stands Out
At IDC, your work helps shape how the world understands technology and where it goes next. You collaborate with curious, high-caliber colleagues who value rigor, integrity, and shared success. As the premier global provider of trusted technology intelligence, IDC equips business and technology leaders with the evidence they need to make confident decisions. Our insights inform strategy, investment, and innovation across industries and regions.
Recognized by IIAR as Analyst Firm of the Year for five consecutive years, IDC sets the standard for credibility and impact. With more than 1,000 analysts worldwide and a truly global perspective, we combine deep expertise with practical relevance. Here, your ideas matter, your voice is heard, and your contributions provide the insights leaders rely on every day. It is meaningful work, backed by a culture that supports growth, collaboration, and long-term career development with a globally respected brand. What We Offer* 15 vacation days (prorated based on start date)
- 12 company-paid holidays
- 6 paid sick days (prorated based on start date; may vary by state)
- Medical, dental, and vision coverage
- 2 floating holidays (prorated based on start date)
- 1 volunteer day
- 401(k) company match (IDC matches 3% on the first 6% of employee contributions)
- Company-paid short-term disability
- Company-paid life insurance
- Company-paid parental leave
Compensation Transparency At IDC, we are committed to fair and equitable pay practices. Employees are compensated equitably for their work, aligned with their skills and experience. Salary and incentive structures are determined through a rigorous process that considers experience, education, certifications, role-specific requirements, internal equity, and verified U.S. market data from an independent third-party partner.
The expected total annual compensation, depending on location and experience, is between $130,000 – $190,000, inclusive of base salary and variable compensation. If this role relocates to a different country, the salary range will be updated to reflect that country's range, rather than a currency conversion of the original range.
Equal Opportunity EmployerIDC is committed to providing equal employment opportunities for all qualified persons. Employment eligibility verification required. We participate in E-Verify.
IDC is currently able to employ remote workers in the following states: Arizona (AZ), California (CA), Colorado (CO), Connecticut (CT), Washington D.C. (DC), Florida (FL), Georgia (GA), Illinois (IL), Indiana (IN), Kansas (KS), Massachusetts (MA), Maryland (MD), Maine (ME), Michigan (MI), Minnesota (MN), Missouri (MO), Mississippi (MS), North Carolina (NC), New Hampshire (NH), New Jersey (NJ), New York (NY), Ohio (OH), Oregon (OR), Pennsylvania (PA), Rhode Island (RI), South Carolina (SC), Tennessee (TN), Texas (TX), Utah (UT), Virginia (VA), Vermont (VT), Washington (WA), and Wisconsin (WI).
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Salary Context
This $130K-$190K 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 IDC Research Inc., 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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $130K to $190K.
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.
IDC Research Inc. AI Hiring
IDC Research Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, US. Compensation range: $190K - $240K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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.
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