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About This Role
Overview:
Job Purpose
Intercontinental Exchange, Inc. (ICE) is seeking an AI and Data Product Manager to lead the transformation of established business workflows by rebuilding them around AI—not by layering AI on top of them. This role owns the strategy, roadmap, and delivery of our AI\-powered applications and data products, sitting at the intersection of business operations, data science, machine learning engineering, and our customers. You will be part of a highly visible team central to ICE’s strategy to analyze mortgage and market data and deliver AI\-driven insights to our clients in a meaningful, responsible, and scalable way.
This is a process\-transformation role first and a feature\-delivery role second. We hire for the ability to decompose workflows and apply AI where it is verifiable—not for prior expertise in any specific industry. A track record of entering an unfamiliar domain, mapping its workflows, and shipping something measurable is the signal we value most.
Responsibilities
- Map and decompose existing business workflows end\-to\-end—identifying steps that are high\-volume, high\-variance, and verifiable—before deciding where AI belongs.
- Reimagine processes around AI rather than bolting AI onto current steps, prioritizing opportunities by the principle of volume, variance, and verifiability.
- Define and own the product vision, strategy, and multi\-quarter roadmap for a portfolio of AI applications and data products aligned to business objectives.
- Size AI opportunities with conservative, evidence\-based ROI assumptions, targeting tasks whose outputs can be reliably graded and avoiding “too much, too fast” over\-commitment.
- Partner closely with data science and ML engineering to translate models—including predictive analytics, NLP, and LLM/generative AI and agentic solutions—into reliable, production\-grade products.
- Design evaluation criteria and acceptance thresholds (“define good before building”); establish evals, blind review panels, and LLM\-as\-judge methods, and monitor for hallucination, bias drift, and model degradation in production.
- Architect human\-in\-the\-loop workflows with expert review designed in, expanding automation only after each phase is proven.
- Productize ICE’s proprietary data assets into well\-defined data products such as APIs, data feeds, datasets, dashboards, and embedded analytics.
- Write clear product requirement documents (PRDs), user stories, and acceptance criteria; maintain and prioritize the product backlog within an Agile/Scrum environment.
- Define success metrics and KPIs (adoption, task success rate, model performance, revenue, ROI) and use data to measure outcomes and continuously improve products.
- Drive change management and adoption—bridging data scientists and business owners and getting non\-technical stakeholders to embrace AI\-changed workflows.
- Champion responsible AI in partnership with data science, risk, and compliance: model governance, bias and fairness, explainability, model risk, and data quality.
- Ensure products meet regulatory and data\-governance requirements relevant to mortgage and financial services (e.g., MISMO, FNMA, FHLMC, GNMA, and applicable privacy standards).
- Communicate roadmap, trade\-offs, progress, and results to cross\-functional partners and executive leadership.
Knowledge and Experience
- 6\+ years of product management experience, with demonstrated work building AI/ML\-powered products, data products, or workflow\-automation solutions (mid\-level is the target tier).
- A demonstrable example of entering a domain cold, mapping its workflows, identifying AI leverage points, and shipping something measurable—industry independent.
- Strong process\-decomposition skills: the ability to map a workflow in detail and score steps by volume, variance, and verifiability.
- Practical AI literacy: working comprehension of LLMs, RAG, agents, prompt engineering, and evaluation design (you do not need to code or train models).
- Empirical mindset: experience designing evals, blind reviews, A/B tests, and acceptance criteria, and iterating against evidence.
- Data literacy, including comfort with SQL and analytics tools to define metrics and inform decisions.
- Change\-management and stakeholder\-translation experience getting non\-technical teams to adopt new, AI\-driven ways of working.
- Ability to recall specific metrics from products you have shipped (e.g., hallucination rate, retrieval precision, task success rate, latency).
- Proven experience working in Agile/Scrum teams and managing a product backlog.
- Excellent written and oral communication, with the ability to explain probabilistic systems to both technical and non\-technical audiences.
Preferred Knowledge and Experience
- Advanced degree (e.g., MBA) or product/Agile certification (e.g., Pragmatic Institute, CSPO, SAFe POPM).
- Hands\-on experience with at least one workflow or process platform—e.g., n8n, Zapier, Make, Workato, Celonis, UiPath.
- Experience launching generative AI / LLM\-based or agentic products or features.
- Background that develops process thinking before AI—operations management, management consulting, analytics/data, or growth/experimentation product management.
- Familiarity with cloud platforms (e.g., AWS) and modern data warehouses such as Snowflake or Databricks.
- Understanding of human\-in\-the\-loop design, model monitoring, drift detection, and responsible\-AI frameworks.
- Exposure to mortgage technology, capital markets, or financial services is helpful but not required.
- A computer science degree. Roughly 60% of working AI PMs do not hold one; demonstrated AI experience is the signal.
- The ability to code or train models. Data literacy and AI comprehension are sufficient.
- Prior expertise in mortgage or financial services. Pattern transfer and rapid domain immersion matter more than industry credentials; domain knowledge can be borrowed empirically from practitioners.
Technical \& Tool Familiarity
- AI/ML Concepts: LLMs, RAG, agents, prompt engineering, and evaluation metrics (precision, recall, F1, hallucination rate, task success rate, latency).
- AI Orchestration (Execution): UiPath Maestro, n8n; awareness of agent protocols such as MCP, A2A, and ACP.
- Workflow Builders (Prototyping): Any of n8n, Zapier, Make, Workato.
- Data Platforms: SQL, Snowflake, Databricks, Spark; data pipelines and ETL concepts.
- Cloud: AWS (or comparable cloud environments).
- Visualization \& BI: SIGMA, Tableau, Microsoft Power BI.
- Product \& Delivery: Jira, Confluence, Productboard; product analytics.
\-: Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Intercontinental Exchange, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Intercontinental Exchange AI Hiring
Intercontinental Exchange has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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