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About This Role
General Information
Locations
- Atlanta, GA
- Boston, MA
- Charlotte, NC
- Hartford, CT
- Miami, FL
- New Brunswick, NJ
- New York, NY
- Philadelphia, PA
- Raleigh, NC
- Washington, DC, US
- White Plains, NY
Business Function Data & analytics Date posted Thursday, September 17, 2026 Ref # JBR0001638 Description and Requirements
Job Description AI/ML Engineer
Please note: This role is noteligiblefor 100% remote work. Employees must live within a commutable distance of a Slalom Office and must be willing to be onsite at the client and/or Slalom office when needed up to 3 days a week. **
Who You’ll Work With
As a modern technology company, our Slalom Technologists are disrupting the market and bringing to life the art of the possible for our clients. We have passion for building strategies, solutions, and creative products to help our clients solve their most complex and interesting business problems. We surround our technologists with interesting challenges, innovative minds, and emerging technologies. Slalom's Data & AI capability focuses on next-level AI and Machine Learning solutions for clients. You'll join a diverse team of engineers, data scientists, and AI thought leaders. We work across modern AI platforms, develop novel AI solutions, and work with our technology partners to push their products into the future.
What You’ll Do
- Design, build, and deploy generative AI and machine learning solutions that solve business problems and improve how people work
- Develop LLM-powered applications using RAG, agents, prompt engineering, tool and function calling, workflow automation, and fine-tuning where appropriate.
- Apply Machine Learning, Deep Learning, and other relevant technologies to assist in building AI tools that solve real business problems.
- Assist in the design, development, presentation, and delivery of traditional ML solutions in areas such as Computer Vision, NLP, Recommendation Systems, etc.
- Stay updated on fundamental AI/ML concepts and emerging trends to contribute to solution discussions and development.
- Contribute to the growth of the AI/ML community within the organization through learning, mentoring, and sharing knowledge.
What You’ll Bring
- 3+ years of experience implementing models or machine learning algorithms into production.
- 2-4 years of professional Managed or IT Professional consulting experience.
- Strong consultative and communication skills, and the ability to convey complex technical concepts to diverse stakeholders.
- Solid technical foundation, combined with constant curiosity to stay on the creative edge of ML/AI solutions.
- Technical and conversational fluency in AI, GenAI, Machine Learning and at least one of the major clouds (AWS, GCP, Azure)
- Hands-on experience building generative AI solutions using LLMs, embeddings, vector search, RAG, agents, prompt engineering, fine-tuning or model adaptation, or similar techniques.
- Experience with AI validation, evaluation, observability, and production quality metrics such as accuracy, groundedness, safety, latency, cost, and drift.
- Experience deploying ML/AI into production, and ability to talk through best practices and pitfalls.
- Understanding of validation frameworks for AI and ML tools, with knowledge of the production-ready offerings on the market for AI validation.
- Excellent python, especially for ML/AI use cases.
- Data Science and/or Data Engineering background preferred.
- Nice to have: + Experience in a sales support role, within Data Science, Machine Learning, or AI. + Familiarity with leading data platforms like Databricks and Snowflake, and their basic services. + Experience implementing MLOps best practices and ML workflow tools, and the ability to translate this experience to creative LLMOps solutions.
About Us
Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working, and meaningful impact.
What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That’s why we prioritize purpose, flexibility, connection, and recognition, so our people can thrive and love what they do, most days.
Compensation and Benefits Slalom prides itself on helping team members thrive in their work and life. As a result, Slalom is proud to invest in benefits that include meaningful time off and paid holidays, parental leave, 401(k) with a match, a range of choices for highly subsidized health, dental, & vision coverage, adoption and fertility assistance, and short/long-term disability. We also offer yearly $350 reimbursement account for any well-being-related expenses, as well as discounted home, auto, and pet insurance.
Slalom is committed to fair and equitable compensation practices. For this role, we are hiring at the following levels and targeted base pay salary ranges:
- Boston, New Jersey, New York City, Washington DC, White Plains: + Consultant: $116,000-$145,000 + Senior Consultant: $145,000-$181,000
- All other locations: + Consultant: $106,000-$133,000 + Senior Consultant: $133,000-$166,000
In addition, individuals may be eligible for an annual discretionary bonus. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time.
We are committed to pay transparency and compliance with applicable laws. If you have questions or concerns about the pay range or other compensation information in this posting, please contact us at: [email protected].
EEO and Accommodations Slalom is an equal opportunity employer and is committed to attracting, developing and retaining highly qualified talent who empower our innovative teams through unique perspectives and experiences. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Slalom will also consider qualified applications with criminal histories, consistent with legal requirements. Slalom welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team or contact [email protected] if you require accommodations during the interview process.
Salary Context
This $106K-$181K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Slalom Consulting, 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($143K) sits 32% below the category median. Disclosed range: $106K to $181K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Slalom Consulting AI Hiring
Slalom Consulting has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect. Positions span Houston, TX, US, Atlanta, GA, US, Denver, CO, US. Compensation range: $150K - $285K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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