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About This Role
General Information
Locations
- Denver, CO
Business Function Product engineering Date posted Thursday, April 23, 2026 Ref # 1744 Description and Requirements
Job Description Mission
As a AI Accelerated Engineering Lead, you will guide teams in the shift toward AI-accelerated engineering and define clear guardrails for how generative AI supports software delivery. You will also own the architecture of client solutions while pioneering the next generation of AI-empowered software development. You’ll lead teams in adopting AI-first engineering practices - bringing clarity, structure, and guardrails to how organizations build with generative AI.
Who You’ll Work With
You’ll join a tightly knit product team-engineers, product leaders, designers, and data practitioners-who co-create modern software with our customers. We move from discovery to delivery together, pairing on code, pressure-testing ideas with real users, and turning learnings into ship-ready increments. You’ll collaborate with customer stakeholders and field teams to bring outside-in insights into the roadmap, and you’ll help enable clients on the practices and tools we use so they can sustain value long after launch. Think craft-driven Builders who care about outcomes, not just output.
What You’ll Do
The AI Accelerated Engineering Lead balances their time between being an effective people leader and setting the technical direction in the delivery of some of our most challenging engagements. You are the advocate for your direct reports. You will develop strong, lasting relationships with each of them as you identify the perfect project match for their skills and objectives. You will partner with clients to understand the most complex technical and functional challenges they are facing. As you iterate on solutions with clients, you will develop implementation and delivery plans that take advantage of emerging technologies at scale. You will coordinate with other capabilities and technical leads in order to ensure that world-class user experiences are understood, validation procedures are aligned, and automation components are delivered. You will help in the pursuit of new business opportunities as a technical expert, detailing proposals for how we would approach delivery. Your expertise in designing and developing holistic software solutions will benefit the rest of Slalom and Build capability as you lead communities and provide mentorship to other Builders. Specifically, you will:
- Lead delivery and implementation: Oversee discovery, delivery, and implementation planning across multiple value streams and ensure teams ship high quality software using clear, repeatable approaches.
- Guide people and technical direction: Support direct reports in their growth, provide technical leadership in pursuits, and contribute knowledge that strengthens engineering practices across Slalom.
- Redefine the AI-first SDLC: Design flows, patterns, and guardrails that integrate generative AI from ideation through operations.
- Lead with context engineering: Apply and guide strategies like prompt templating, embeddings, retrieval-augmented generation, and data grounding to ensure AI-driven development is accurate, reliable, and safe.
- Manage GenAI-driven teams: Orchestrate multi-developer environments where AI agents and engineers work side by side-ensuring productivity, quality, and responsible adoption.
- Ship AI-enabled platforms: Build and codify repeatable practices such as evaluation harnesses, code-review agents, context packs, and CI/CD hooks.
- Stay hands-on: Own product architecture while prototyping and hardening AI-driven solutions using modern DevOps and observability practices.
- Enable and transform: Coach teams and client stakeholders on AI-accelerated ways of working; translate complex AI concepts into clear business outcomes.
What You’ll Bring
- Leadership & communication: Ability to lead teams, mentor engineers, and articulate value to both technical and business audiences.
- Experience: 8+ years building and operating production software, with proven leadership in system or platform architecture.
- Hands-on AI-accelerated development: Daily use of agentic IDEs and advanced tooling (e.g., GitHub Copilot, Cursor, Claude Code) beyond basic chat interfaces.
- Proven track record with GenAI integration: Experience delivering solutions where multiple developers collaborated with generative AI and AI agents using context engineering strategies.
- Architecture expertise: Deep knowledge of APIs, microservices, eventing, reliability, and security.
- Programming skills: Proficiency in languages like TypeScript/Node, Python, or Java; comfort with React/Next and modern frameworks.
- Cloud & DevOps: Strong background with AWS/Azure/GCP, Kubernetes, CI/CD, IaC, and production observability.
- Bonus Skills
- AI orchestration frameworks (LangChain, LlamaIndex), vector databases, or model routing experience.
- MLOps, AI governance, or safety practices.
- Contributions to open source or thought leadership in AI-accelerated engineering.
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 collaborate with clients to bring powerful customer experiences, innovative ways of working, and new products and services to life. We are trusted by leaders across the Global 1000, many successful enterprise and mid-market companies, and 500+ public sector organizations to improve operations, drive growth, and create value. At Slalom, we believe that together, we can move faster, dream bigger, and build better tomorrows for all.
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 these locations:
San Francisco
- Senior Principal: $229,000-$281,000
Washington DC
- Senior Principal: $210,000-$257,000
Denver
- Senior Principal: $193,000-$236,000
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 $193K-$236K range is above 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. Disclosed range: $193K to $236K.
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
AI roles in Denver pay a median of $197,900 across 126 tracked positions. That's 7% below the national 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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