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About This Role
Join us at Entrust
At Entrust, we’re shaping the future of identity centric security solutions. From our comprehensive portfolio of solutions to our flexible, global workplace, we empower careers, foster collaboration, and build solutions that help keep the world moving safely.
Get to Know Us
Headquartered in Minnesota, Entrust is an industry leader in identity\-centric security solutions, serving over 150 countries with cutting\-edge, scalable technologies. But our secret weapon? Our people. It’s the curiosity, dedication, and innovation that drive our success and help us anticipate the future.
Entrust is seeking a Principal AI \& Analytics Data Architect to join our Corporate IT organization to design and manage data systems, ensuring data quality, and providing technical leadership in data architecture. The position reports to the Chief Information Officer and is based in Shakopee, Minnesota, United States.
Responsibilities
- Implement and maintain a Data Catalog.
- Standardize metadata tagging and data lineage to ensure AI teams clearly understand data sources.
- Define a Semantic Layer (or metrics store) so key metrics are calculated consistently across AI models and dashboards.
- Ensure data is optimized for AI consumption, including vectorization readiness, handling unstructured content (e.g., PDFs, images), and developing “gold” standard datasets.
- Enterprise AI \& Analytics Architecture (Primary)
+ Define and own the enterprise AI \+ analytics reference architecture
+ Architect data platforms that support:
- Executive analytics and BI
- Machine learning and generative AI
- AI agents and copilots
+ Establish standards across:
- Data lakes, warehouses, and lakehouse patterns
- Semantic layers and governed metrics
- Feature stores and vector data stores
- Ensure the architecture scales for global, multi‑business use cases
- Data Governance, Trust \& Risk (By Design)
+ Architect governance‑first data and AI patterns, not bolt‑ons
+ Ensure analytics and AI workloads comply with:
- Data classification and retention policies
- Privacy, residency, and regulatory requirements
+ Integrate and operationalize:
- Data cataloging and lineage
- DSPM and sensitive data monitoring
- Be a trusted partner to Security, Risk, Legal, and Compliance
- Platform \& Data Product Leadership
Shift the organization from:
“Data pipelines”
+ - Data products and governed assets
+ Define reusable patterns for:
- Ingest (ERP, CRM, SaaS, logs)
- Transform (ELT/semantic modeling)
- Serve (BI, APIs, AI, Copilots)
+ Enable safe, governed access to data for:
- Analysts
- Data scientists
- AI agents and copilots
- Executive \& Architecture Leadership
+ Act as the executive‑facing authority on data, analytics, and AI
+ Present architecture, risks, and tradeoffs to:
- CIO
- Executive Leadership Team
+ Set and enforce data and analytics standards across IT and product teams
+ Mentor senior architects, analytics leaders, and engineers
Basic Qualifications
- 12–15\+ years in data architecture, analytics leadership, or platform design
- Proven experience in designing and scaling:
- Enterprise analytics platforms
- AI‑ready data foundations
- Implement and maintain a Data Catalog platform
- Strong command of:
- Cloud data ecosystems (AWS preferred)
- Analytics architectures (BI, semantic layers, metrics)
- AI‑specific data patterns (RAG, feature stores, vector data)
- Deep understanding of governance, privacy, and security‑by‑design
- Experience operating in complex, regulated, or security‑sensitive environments
- Strong analytical and problem\-solving skills.
- Excellent communication and interpersonal abilities.
- Ability to work independently and collaboratively in a team environment.
Preferred Qualifications:
- Prior ownership of an Analytics or Data function
- Experience modernizing legacy analytics stacks
- Expertise bridging:
- IT architecture
- Analytics delivery
- Business decision‑making
- Background working directly with executive leadership
- Familiarity with cloud platforms (e.g., AWS, Azure)
- Knowledge of data security and privacy legislation.
At Entrust, we don’t just offer jobs – we offer career journeys. Here is what you can expect when you join our team:
- Career Growth: Whether you’re a budding developer or a seasoned expert, we’re invested in your professional journey. With learning\-forward initiatives and exciting challenges, your growth is our priority.
- Flexibility: Life is all about balance. Whether you’re remote, hybrid, or on\-site, we offer flexible options that fit your lifestyle.
- Collaboration: Here, your voice matters. Our teams thrive on sharing ideas, brainstorming solutions, and working together to build a better tomorrow.
We believe in securing identities—but it doesn’t stop there. At Entrust, we’re passionate about valuing all identities. Our culture is built on diversity, inclusion, and respect. From unconscious bias training for our leaders to global affinity groups that connect colleagues across the globe, we’re creating a community where everyone is encouraged to be themselves.
Ready to Make an Impact?
If you’re excited by the prospect of innovating, growing your career, and collaborating in a dynamic environment, Entrust is the place for you. Join us in making a difference. Let’s build a more secure world—together.
Apply today!
For more information, visit www.entrust.com. Follow us on, LinkedIn, Facebook, Instagram, and YouTube
Compensation Range:
The anticipated starting base pay for this position is: $160,797\-$235,836 per year (in the primary posting location). Actual compensation will be determined based on geographic location, education, skills and experience. This position is also eligible for the company’s discretionary annual incentive plan. In addition to your pay, Entrust offers eligible colleagues and their dependents comprehensive health and well\-being programs which include medical, vision, dental, a generous 401(k) matching contribution, life and disability insurance, mental health coaching, virtual fitness programs, paid personal time off plus 12 paid holidays, parental leave and education reimbursement. Please speak with the recruiter for more details. Note: Benefit and Compensation programs are subject to eligibility requirements and other terms of the applicable plan or program. Entrust has the right to end, suspend or amend any of its plans at any time in whole or in part.*For US roles, or where applicable:*
Entrust is an EEO/AA/Disabled/Veterans Employer
*For Canadian roles, or where applicable:*
Entrust values diversity and inclusion and we are committed to building a diverse workforce with wide perspectives and innovative ideas. We welcome applications from qualified individuals of all backgrounds, and we strive to provide an accessible experience for candidates of all abilities.
*If you require an accommodation, contact* *[email protected]**.*
Recruiter:
Steve Donahue
Salary Context
This $160K-$235K range is above 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 Entrust Corporation, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($198K) sits 9% above the category median. Disclosed range: $160K to $235K.
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.
Entrust Corporation AI Hiring
Entrust Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Shakopee, MN, US. Compensation range: $235K - $235K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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