Interested in this AI/ML Engineer role at Comcast?
Apply Now →Skills & Technologies
About This Role
Make your mark at Comcast \- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award\-winning technology team that turns big ideas into cutting\-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on\-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.) Job Summary
We are seeking a visionary Senior Technologist, Data \& Agentic AI Enablement to shape the future of our enterprise data ecosystem and accelerate the next generation of AI\-driven experiences. This enterprise leadership role is responsible for two complementary missions: Preparing enterprise data for an Agentic AI future, ensuring data is trusted, discoverable, semantically rich, governed, and consumable by AI agents. Transforming data engineering through Agentic AI, embedding AI across the data engineering lifecycle to improve productivity, quality, reliability, and speed of delivery. The successful candidate will define the architecture, standards, and engineering patterns that enable AI to become an active participant in the design, development, testing, operation, and optimization of enterprise data platforms. This highly influential role partners closely with Data Engineering, Data Platforms, Enterprise Architecture, Security, Product, and AI teams to accelerate data modernization and AI\-enabled business transformation.Job Description
Agentic AI Transformation of Data Engineering:
--------------------------------------------------
Lead the strategy for integrating Agentic AI across the enterprise data engineering lifecycle, enabling AI\-assisted development, operations, and platform management.
Responsibilities include:
- Define the enterprise roadmap for incorporating AI agents into data engineering workflows, platform operations, and software delivery.
- Partner with platform engineering teams to integrate AI capabilities into CI/CD, Infrastructure\-as\-Code (IaC), and DevSecOps practices.
- Evaluate emerging agent frameworks, copilots, and autonomous engineering platforms for enterprise adoption.
- Establish best practices and governance for AI\-assisted engineering and operational processes.
Enterprise Data Strategy for AI \& Agentic Systems:
-------------------------------------------------------
Develop and drive the enterprise strategy for preparing data assets to support AI, Generative AI, and Agentic AI use cases.
Responsibilities include:
- Define principles and reference architectures that enable AI agents to discover, access, understand, and act upon enterprise data safely and effectively.
- Partner with business and technology leaders to identify high\-value opportunities for Agentic AI solutions.
- Establish enterprise standards that align data, AI, and business strategies.
Data Architecture \& AI Readiness
-------------------------------------
Lead architectural efforts to ensure enterprise data is optimized for both human and AI consumption.
Responsibilities include:
- Establish standards that ensure data is:
+ Discoverable
+ Well\-described and semantically rich
+ Governed and trusted
+ Accessible through standardized interfaces
+ Consumable by both people and AI agents
- Drive adoption of metadata\-driven architectures, semantic models, knowledge graphs, and business ontologies.
- Ensure enterprise data products support machine\-to\-machine interactions in addition to traditional analytics use cases.
Agentic Data Enablement:
----------------------------
Define the frameworks that enable AI agents to effectively interact with enterprise data and knowledge assets.
Responsibilities include:
- Establish standards for exposing enterprise data through APIs, semantic layers, data products, and retrieval systems.
- Partner with platform teams to develop capabilities supporting:
+ Retrieval\-Augmented Generation (RAG)
+ Agent orchestration platforms
+ Tool and API discovery
+ Vector\-based retrieval architectures
+ Context management and memory frameworks
- Develop patterns that allow AI agents to access enterprise knowledge securely and responsibly.
Data Governance \& Trust:
-----------------------------
Ensure governance and trust frameworks evolve to support autonomous and AI\-assisted decision making.
Responsibilities include:
- Establish controls for data lineage, provenance, quality, explainability, and auditability.
- Partner with Security, Privacy, and Risk teams to implement responsible AI controls and secure data access practices.
- Define trust frameworks that enable AI agents to operate within approved business guardrails.
Semantic Layer \& Knowledge Management:
-------------------------------------------
Drive the development of enterprise semantic capabilities that improve data accessibility and AI reasoning.
Responsibilities include:
- Lead development of enterprise semantic models and shared business definitions.
- Improve metadata quality, business context, and knowledge accessibility across the organization.
- Advance enterprise knowledge management practices that enhance AI reasoning, discovery, and decision support.
Platform \& Ecosystem Alignment:
------------------------------------
Collaborate across teams to ensure enterprise platforms support AI\-native consumption patterns.
Responsibilities include:
- Partner with Data Platform, Engineering, Analytics, and Product teams to align technology roadmaps.
- Collaborate with BI and analytics teams to maintain consistent business metrics and semantic definitions across human and AI consumers.
- Influence technology investments that enable future AI and Agentic AI capabilities.
Innovation \& Thought Leadership
------------------------------------
Serve as a strategic thought leader on AI readiness, data modernization, and enterprise architecture.
Responsibilities include:
- Monitor emerging trends in AI, Agentic Systems, Data Architecture, and Knowledge Management.
- Evaluate innovative technologies and identify strategic adoption opportunities.
- Advise executive leadership on enterprise AI strategy and future\-state architecture.
Qualifications
------------------
- \+ years of experience in Data Architecture, Data Engineering, Enterprise Architecture, or related technology disciplines.
- Proven experience designing and scaling enterprise data ecosystems.
- Experience supporting AI, machine learning, advanced analytics, or data modernization initiatives.
- Demonstrated success influencing outcomes across large, matrixed organizations.
Technical Expertise
-----------------------
- Deep understanding of modern data architectures, including data warehouses, data lakes, lakehouses, and data mesh concepts.
- Expertise in metadata management, semantic modeling, data governance, and data product design.
- Strong understanding of APIs, event\-driven architectures, and interoperability standards.
- Experience with AI technologies, including LLMs, RAG architectures, vector databases, agent frameworks, and AI orchestration platforms.
- Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud\-native architectures.
Leadership \& Communication
-------------------------------
- Strong executive communication and stakeholder management skills.
- Ability to translate emerging technologies into practical enterprise strategies.
- Proven ability to lead through influence across business and technology organizations.
- Demonstrated thought leadership in data, AI, or enterprise architecture domains.
### Preferred Candidate Profile :
A strategic technology leader with deep expertise in enterprise data architecture and a passion for advancing AI adoption. This individual combines strong technical vision, architectural leadership, and business acumen to help the organization build a trusted, AI\-ready data foundation while transforming the way engineering teams work through Agentic AI.
Disclaimer:
This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.
Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.
Skills:
Data Engineering; AI Adoption; Agentic AI; Data Architecture Development; Data Strategies; Enterprise Data
Salary:
Pay Range: This job can be performed in Virginia, and District of Columbia with a Pay Range of $224,190\.44 \- $351,571\.37
Comcast intends to offer the selected candidate base pay within this range, dependent on job\-related, non\-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non\-sales positions are eligible for a Bonus. Additionally, Comcast provides best\-in\-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That’s why we provide an array of options, expert guidance and always\-on tools, that are personalized to meet the needs of your reality \- to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.
Education
Bachelor's Degree
While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.
Relevant Work Experience
15 Years \+
Salary Context
This $224K-$351K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Comcast, 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. This role's midpoint ($287K) sits 34% above the category median. Disclosed range: $224K to $351K.
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.
Comcast AI Hiring
Comcast has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span Philadelphia, PA, US, New York, NY, US, Reston, VA, US. Compensation range: $152K - $351K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.