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About This Role
At AlixPartners, we solve the most complex and critical challenges by moving quickly from analysis to action when it really matters; creating value that has a lasting impact on companies, their people, and the communities they serve. By understanding, respecting, and honoring the needs of our employees, clients, and communities, AlixPartners actively promotes an inclusive environment. We strongly believe in the value that diversity brings to our experiences and are committed to the perpetual enhancements of initiatives, policies, and practices. We hold ourselves accountable by providing space for authenticity, growth, and equity for everyone.
Location: Southfield, MI, Chicago, IL, Dallas or Houston, TX
Assignment: Expected to range 8 – 12 months
*MUST BE CURRENTLY AUTHORIZED TO WORK IN THE UNITED STATES. NO VISA OR IMMIGRATION SPONSORSHIP FOR THIS ROLE, NOW OR IN THE FUTURE. (e.g., H\-1B, STEM OPT, TN, etc.)*
About the Role
We're seeking an Information Security Lead – AI \& Emerging Technology to join our growing Information Security team and help embed security into our DevOps and cloud practices. This role will work together with DevOps, Cloud Infrastructure, and Application Engineering teams to design and implement secure, scalable cloud solutions across Azure and AWS environments.
The ideal candidate brings deep cloud expertise, a passion for automation, and hands\-on experience building secure\-by\-design architectures. You will also help implement AI security controls and support our organization's broader AI adoption strategy.
The Information Security Lead – AI \& Emerging Technology is a full\-time, *contract* role reporting to the Deputy Chief Information Security Officer. *Benefits or paid relocation are not included in this contract position.*
What you'll do
- Partner with DevOps and engineering teams to integrate security throughout the CI/CD pipeline and Infrastructure as Code (IaC) deployments, including hands\-on implementation of security tooling, reusable pipeline templates, security gates, exceptions, and troubleshooting.
- Design, implement, and maintain Azure Policies and guardrails for continuous compliance and drift detection, including policy initiatives, assignments, and policy\-as\-code practices.
- Develop secure cloud configurations and architectures leveraging PaaS services across Azure (and, to a lesser extent, AWS), including managed identities, Key Vault, private endpoints, RBAC, logging/diagnostics, and secure networking patterns.
- Key contributor to threat modeling and building defensible architecture
- Collaborate with architecture and infrastructure teams to define and implement AI\-specific security controls.
- Monitor regulatory and industry developments and align cloud security practices with organizational policy and compliance goals.
- Support detection and response teams with insights into cloud and DevOps security events.
- Additional responsibilities as identified. This description is not designed to encompass a comprehensive listing of required activities, duties, or responsibilities.
What you'll bring
- Minimum five (5\) years of hands\-on experience in cloud security engineering or a DevSecOps focused role, with direct experience implementing security controls in production Azure DevOps/GitHub CI/CD pipelines, including YAML templates, security gates, exceptions, and troubleshooting.
- Strong working knowledge of Microsoft Azure, Azure Policy (Enterprise Policy as Code \- EPAC), and related cloud governance tooling (e.g. AWS Config), with the ability to assess Defender/Secure Score recommendations, determine appropriate remediation, and help prevent recurrence.
- Demonstrated proficiency in Infrastructure as Code (IaC) tools (Terraform, Bicep, ARM templates, etc.).
- Practical and demonstrated experience deploying and securing PaaS environments and cloud\-native services in multi\-tenant environments
- Solid understanding of cloud networking, identity management, zero trust principles, and federation.
- Familiarity with AWS cloud fundamentals and security features.
- Preferred qualifications:
- + Experience developing or securing AI/ML workloads in cloud environments.
+ Relevant certifications such as AZ\-500 (Azure Security Engineer Associate), CCSP, SANS GCAD, or Terraform certifications.
+ Knowledge of container security (Kubernetes, AKS, EKS) and shift\-left security approaches.
+ Experience with Defender for Cloud tools (Defender for Containers, Defender for DevOps, workload protections, etc.)
- Excellent written and verbal communication skills in English, with the ability to convey complex information clearly to stakeholders; ability to translate security requirements into actionable DevOps practices.
- Highly organized with excellent organizational skills with experience operating in time\-sensitive, ambiguous environments, balancing competing priorities with sound judgment and discretion.
- Core working hours are generally 8:30 AM – 5:30 PM, Monday \- Friday; willingness to work outside of normal U.S. business hours, and as unique projects/needs arise
- Ability to work full time in an office and remote environment; physically able to sit/stand at a computer and work in front of a computer screen for significant portions of the workday
- Must become familiar with, and promote and abide by, our Core Values as defined by the AlixPartners' Code of Conduct and foster an inclusive environment with people at all levels of an organization
*AlixPartners is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to, among other things, race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a protected veteran, or disability. AlixPartners is a proud Gold\-Level award\-winning Veteran Friendly Employer.*
\#LI\-KL2
\#LI\-Hybrid
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 AlixPartners, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
AlixPartners AI Hiring
AlixPartners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Detroit, MI, 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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