Interested in this AI/ML Engineer role at Exadel?
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Why Join Exadel
We're an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients, including HBO, Microsoft, Google, and Starbucks.
From AI platforms to digital transformation, we partner with enterprise leaders to build what's next.
What powers it all? Our people are ambitious, collaborative, and constantly evolving.
About the Client
A global technology leader specializing in search technology, digital advertising, cloud computing, and artificial intelligence. Delivering enterprise cloud infrastructure and software solutions, the organization builds scalable consumer and enterprise AI experiences. Today, the company leverages foundational AI models and advanced machine learning to power digital ecosystems for billions of global users.
What You'll Do
- Conduct formal, end-to-end written privacy reviews for products, features, research publications, data pipelines, and experimental prototypes
- Evaluate privacy risks and recommend practical safeguards aligned with internal standards and applicable global regulations
- Manage reviews from initial discovery and risk identification through remediation and final approval
- Provide strategic guidance and support the escalation of complex or high-risk privacy matters
- Maintain accurate, detailed, and audit-ready assessment documentation
- Assess the privacy implications of AI, machine learning, generative AI, and Large Language Model systems
- Evaluate data ingestion, prompt inputs, context windows, model outputs, training workflows, inference processes, and human-in-the-loop oversight
- Identify potential risks involving personal data, data leakage, memorization, secondary use, data retention, and inappropriate model outputs
- Recommend appropriate privacy controls for experimental and production AI systems
- Support the responsible development and launch of emerging AI capabilities
- Review technical design documents, Product Requirements Documents, Business Requirements Documents, system architecture diagrams, data-lineage documentation, and academic publications
- Identify privacy touchpoints, data dependencies, and potential gaps across product and data-processing lifecycles
- Translate privacy, legal, and compliance requirements into practical and actionable guidance for product and engineering teams
- Evaluate complex product scenarios in which requirements, risks, or ownership may not yet be clearly defined
- Partner with product managers, software engineers, data scientists, legal teams, and privacy stakeholders to incorporate privacy controls throughout the product lifecycle
- Evaluate controls relating to notice and consent, transparency, user choice, data portability, data minimization, access, and retention
- Review data-collection practices, consent flows, access controls, privacy policies, and user-facing privacy notices
- Help teams make informed product decisions that balance innovation, regulatory obligations, and user trust
- Identify gaps in existing privacy-review processes and recommend operational improvements
- Develop and refine privacy frameworks, standard operating procedures, guidelines, templates, and review criteria
- Support operational reporting and program-management activities across the privacy lifecycle
- Use custom tools and AI technologies to automate workflows, improve review efficiency, and optimize governance operations
- Help establish consistent and scalable privacy-review practices across research and innovation teams
What You Bring
- Extensive hands-on experience conducting formal written privacy reviews, assessments, or impact analyses
- Product-focused privacy experience within technology, digital platforms, cloud services, AI, or similarly complex environments
- An active IAPP certification in at least one of the following: Artificial Intelligence Governance Professional, Certified Information Privacy Professional/United States, Certified Information Privacy Professional/Europe, Certified Information Privacy Manager
- Practical knowledge of AI and machine learning data-processing workflows, generative AI applications, and Large Language Models
- Understanding of privacy risks associated with model-training data, user inputs, inference processes, and model outputs
- Deep knowledge of privacy principles, including notice and consent, transparency, user choice, data portability, data minimization, and retention
- Knowledge of relevant privacy and AI regulatory frameworks, including GDPR, CCPA, and the EU AI Act
- Ability to interpret complex software-design documents, system architectures, data flows, and product requirements
- Experience managing multiple reviews and competing priorities under tight deadlines
- Strong judgment and the ability to make well-supported recommendations in ambiguous or rapidly evolving environments
- Exceptional written and verbal English communication skills
- Ability to translate legal and compliance requirements into clear, actionable technical guidance
- Strong stakeholder-management and influencing skills across product, engineering, legal, and governance teams
Nice to have
- Familiarity with enterprise privacy-review, governance, and product-launch tools
- Experience supporting privacy governance for research, experimental products, or emerging technologies
- Experience working with cross-functional legal, ethics, privacy, product, and engineering governance bodies
- Experience automating privacy-review or governance workflows using AI or custom tooling
- A degree in Law, Computer Science, Data Governance, Information Security, or a related discipline
- Additional privacy, security, risk, or AI-governance certifications
English level
Advanced
Work Type: Remote
Location: United States
Working Hours: Alignment with west coast US time zone
Legal & Hiring Information
- Compensation Transparency: The expected compensation range for this role is $50–100/hr USD, with eligibility for commission, bonus, or other incentive compensation where applicable. Actual compensation will be determined based on experience, skills, qualifications, geographic location within the United States, and business needs.
- Location Eligibility: This role is open to candidates residing in the United States.
- Experience Requirements: We welcome applicants with relevant experience regardless of where it was obtained. US work experience is not required.
- Use of Artificial Intelligence in Hiring: We may use automated tools or artificial intelligence systems to support the screening, assessment, or selection of applicants as part of our hiring process.
- Benefits Summary: US-based employees are eligible to participate in Exadel's benefits programs, which may include comprehensive health, dental, and vision coverage; life and disability insurance; retirement savings programs; paid time off; paid holidays; and other wellness or voluntary benefit programs, subject to plan terms and eligibility requirements.
- Equal Employment Opportunity: Exadel is proud to be an Equal Opportunity Employer committed to inclusion across minority, gender identity, sexual orientation, disability, age, and more. We do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, status as a protected veteran, or any other protected characteristic under applicable federal, state, and local laws.
- Accessibility and Accommodation: We are committed to providing accommodations throughout the recruitment process in accordance with the Americans with Disabilities Act (ADA) and other applicable federal, state, and local accessibility laws. Reasonable accommodations are available to enable individuals with disabilities to perform essential functions. If you require accommodation at any stage of the hiring process, please contact us.
- Data Privacy Notice: For applicants residing in California, please note that personal data collected during the application process is subject to the California Consumer Privacy Act (CCPA) and will be processed in accordance with our applicant privacy notice.
- Disclaimer: Please note: this job description is not exhaustive. Duties and responsibilities may evolve based on business needs. The offer is not binding until a signed contract is in place.
Exadel Culture
We lead with trust, respect, and purpose. We believe in open dialogue, creative freedom, and mentorship that helps you grow, lead, and make a real difference. Ours is a culture where ideas are challenged, voices are heard, and your impact matters.
Salary Context
This $104K-$208K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Exadel, 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 in Demand for This Role
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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($156K) sits 26% below the category median. Disclosed range: $104K to $208K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Exadel AI Hiring
Exadel has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US. Compensation range: $208K - $208K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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