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About This Role
At EMC, we’re all about working together to make an impact. As part of our team, you’ll have the opportunity to grow, contribute, and gain experience that matters. We strive to be caring leaders, close partners, and responsive experts—always supporting each other to do our best work. Join us, and let’s improve lives together.
*This position is eligible to work from home anywhere in the US*
The Enterprise AI Platform Director provides strategic and technical leadership for the enterprise AI platform, establishing the capabilities, patterns, guardrails, and operating model that enable product and engineering teams to deliver AI-enabled solutions safely and efficiently. Defines and maintains the AI platform capability roadmap in alignment with enterprise priorities and product roadmaps. Leads a team responsible for reusable AI runtime, model and prompt management, agent orchestration, tool and Model Context Protocol (MCP) enablement, Application Programming Interface (API) and MCP gateway capabilities, observability, and platform operations. Provides technical direction and decision-making without requiring the leader to serve as the deepest hands-on implementation expert in every AI technology.
Essential Functions:
- Defines the enterprise AI platform strategy and capability roadmap.
- Aligns platform investments to enterprise priorities, product roadmaps, recurring engineering needs, and emerging AI capabilities.
- Establishes a composable platform direction that enables teams to reuse trusted capabilities.
- Evaluates opportunities, sequencing, value, cost, risk, and adoption needs.
- Communicates platform strategy and direction to technology and business stakeholders.
- Partners with product management and business leaders to understand prioritized problems and desired outcomes while aligning shared technology capabilities to accelerate those priorities.
- Leads the design, delivery, and evolution of shared AI platform capabilities, including agent runtime and orchestration patterns, approved model access and routing, prompt lifecycle management, tool and MCP enablement, API/MCP gateways, domain capability management, observability, evaluation, and platform operations.
- Establishes architectural direction, technical standards, and guardrails while empowering platform engineers and architects to own detailed implementation.
- Creates patterns for product and engineering teams to publish, register, secure, discover, and consume reusable APIs, MCP servers, tools, prompts, agents, and other composable capabilities.
- Builds enterprise adoption and advances the platform toward governed self-service.
- Partners with product and engineering teams on enablement.
- Demonstrates emerging AI capabilities and the art of possible to product and business leaders, identifies recurring needs that should become reusable platform capabilities, and develops reference patterns, demonstrations, documentation, training, and onboarding approaches.
- Evolves the capability ecosystem so appropriate business users can increasingly experiment with and compose bounded workflows from trusted enterprise capabilities.
- Embeds governance, security, privacy, compliance, responsible AI, human oversight, auditability, cost management, and operational controls into shared platform capabilities.
- Partners with governance, security, risk, legal, data, and architecture functions to translate enterprise policy into practical guardrails that enable innovation within established boundaries.
- Researches and evaluates emerging AI technologies, vendors, standards, and industry trends; sponsors proofs of concept and proofs of value to inform platform direction; and assesses enterprise fit, total cost of ownership, risk, and value.
- Leads and develops a high-performing technical team accountable for reliable operation and continuous evolution of the enterprise AI platform.
Directs and monitors the workload and performance of the team and performs all duties of a people leader at EMC. *
Education & Experience:
- Bachelor’s degree, preferably in computer science, management information systems, engineering, or related field, or equivalent relevant experience
- Ten years of progressive technology experience in platform engineering, architecture, software engineering, data and analytics, AI/ML platforms, or enterprise technology leadership
Cloud, architecture, data, AI, or related technical certifications preferred *
Knowledge, Skills & Abilities:
- Excellent knowledge of generative AI and agentic concepts, including models, prompts, agents, tools, orchestration, context, evaluation, and responsible AI
- Strong leadership skills with the ability to develop high-performing teams and establish platform or shared-service strategies across multiple product or engineering groups
- Excellent strategic and analytical skills with the ability to translate enterprise demand, product priorities, technology trends, risk, and decisions into capability roadmaps that influence stakeholders and deliver incremental value
- Excellent knowledge of modern cloud platforms including AWS, Azure, GCP
- Strong understanding of platform engineering, enterprise integration, identity and access, observability, reliability, and software delivery practices
- Ability to establish technical direction and make sound architecture and platform decisions without being the hands-on implementation expert in every underlying technology
- Strong understanding of governance, security, privacy, compliance, auditability, and cost-management considerations for enterprise AI
- Ability to demonstrate the art of possible and translate complex AI concepts into clear implications for technical and nontechnical audiences
- Excellent written and verbal communication skills, including presentation and facilitation skills
- Excellent planning and organizational skills
The hiring salary range for this position will vary based on geographic location, falling within either of the following:
$141,433 - $203,218 or $156,324 - $223,550 A hiring range represents a subset of the full salary range. The actual salary will depend on several factors, including relevant education, skills, and experience of an applicant, geographic location, and business needs.
For information relating to the benefits EMC Team Members receive as part of a comprehensive rewards package, please visit www.emcins.com/careers .
Our employment practices are in accordance with the laws that prohibit discrimination due to race, color, creed, sex, sexual orientation, gender identity, genetic information, religion, age, national origin or ancestry, physical or mental disability, medical condition, veteran status, active military status, citizenship status, marital status or any other consideration made unlawful by federal, state, or local laws.
All of our locations are tobacco free including in company vehicles.
Salary Context
This $141K-$223K range is above 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 EMC Insurance, 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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($182K) sits 14% below the category median. Disclosed range: $141K to $223K.
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.
EMC Insurance AI Hiring
EMC Insurance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $223K - $223K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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.
Frequently Asked Questions
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