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About This Role
General Information
Locations
- Austin, TX
- Chicago, IL
- Columbus, OH
- Dallas, TX
- Detroit, MI
- Houston, TX
- Kansas City, MO
- Minneapolis, MN
- Nashville, TN
- St. Louis, MO
Business Function Data & analytics Date posted Friday, June 12, 2026 Ref # JBR0001244 Description and Requirements
Job Description
Who You’ll Work With
Please note: This role is not eligible for 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. This role could require travel to support the region between 10-20%.
As a modern technology company, Slalom technologists are disrupting the market and bringing to life the art of the possible for our clients. We have a passion for building strategies, solutions, and creative products that help clients solve their most complex and interesting business problems.
Slalom’s AI Systems & Platforms team focuses on next-level AI, machine learning, and production AI systems for enterprise clients. You’ll join a diverse team of engineers, data scientists, architects, Responsible AI specialists, and AI thought leaders who work across modern cloud, data, and AI platforms. We build practical, scalable AI solutions, partner with leading technology platforms, and help clients move from AI ambition to production outcomes.
What You’ll Do
As an AI Engineer, you will be a hands-on builder responsible for designing, building, evaluating, and deploying production-grade AI systems for clients. You will contribute to solution architecture, own implementation workstreams, write production-quality code, and clearly articulate technical decisions, delivery progress, risks, and business value to client stakeholders.
You will:
- Design and build AI-enabled applications and systems, including LLM-powered applications, retrieval-augmented generation solutions, agentic workflows, APIs, data pipelines, and cloud-native services.
- Use modern AI platforms, hyperscaler services, data platforms, and software engineering practices to deliver reliable, secure, maintainable solutions.
- Contribute to AI solution architecture, including application design, cloud service selection, data integration patterns, model/service integration, evaluation strategy, deployment approach, and production operating considerations.
- Own implementation workstreams from design through delivery, including backlog refinement, technical design, coding, testing, integration, deployment, documentation, and client handoff.
- Work hands-on with at least one major hyperscaler such as AWS, Azure, or GCP, and at least one major enterprise data platform such as Databricks or Snowflake.
- Partner with clients to understand business processes, technical environments, data constraints, and user needs, then translate those inputs into deployable AI solutions.
- Build and integrate AI systems with enterprise data sources, applications, APIs, workflow tools, and existing client technology ecosystems.
- Apply practical data science and machine learning methods where appropriate, including classification,clustering, segmentation, recommendations, NLP, semantic search, experimentation, and model evaluation.
- Develop evaluation approaches for AI systems, including accuracy, groundedness, relevance, reliability, latency, cost, usability, and business impact.
- Collaborate with Responsible AI specialists to apply responsible AI practices, including appropriate human oversight, risk awareness, validation, documentation, and safe use of AI development tools.
- Responsibly use AI coding assistants and agentic development tools such as Claude Code, OpenAI Codex,Antigravity,Cursor, GitHub Copilot or comparable tools to accelerate delivery while maintaining code quality, security, testing, and human review.
- Create reusable assets such as reference architectures, accelerators, code templates, demos, implementation patterns, and enablement materials.
- Communicate technical concepts, architecture decisions, implementation tradeoffs, delivery progress, risks, and outcomes to client stakeholders ranging from engineers to executives.
- Mentor peers and clients on AI engineering practices, production delivery patterns, responsible AI basics, and practical use of modern AI development tools.
- Stay current on emerging AI engineering patterns, hyperscaler capabilities, data platform features, LLM application architectures, agentic workflows, and production AI practices.
What You’ll Bring
- 3+ years of experience designing and building data, AI, software, or machine learning solutions in real-world business environments.
- Hands-on experience building production-quality software using Python and modern software engineering practices.
- Hands-on experience with statistical and data mining software packages inPython (e.g. SciPy, NumPy, Pandas, SciKit-Learn, glmnet, caret, dplyr) and a knowledge of a variety of statistical modeling and machine learning algorithms as well as their practical application.
- Experience contributing to technical architecture and owning implementation workstreams in client-facing or cross-functional delivery environments.
- Experience delivering AI or ML solutions beyond notebook prototypes, including deployment, integration, testing, monitoring, and operational support.
- Experience with Azure OpenAI, Azure AI Foundry, AWS Bedrock, Google Vertex AI, Databricks Mosaic AI, Snowflake Cortex, or comparable enterprise AI platforms.
- Experience with vector databases; embeddings models; search platforms; AI Agentbuildand orchestration frameworks; LLM evaluation tools; and AI observability tooling.
- Experience with APIs, SQL, data pipelines, ETL/ELT, cloud services, and enterprise data integration patterns.
- Experience with LLM application patterns such as RAG, embeddings, semantic search, prompt orchestration, tool/function calling, or agentic workflows.
- Practical understanding of model and AI-system evaluation, including validation methods, test datasets, quality metrics, error analysis, and production feedback loops.
- Working knowledge of responsible AI basics, including privacy, security, bias, explainability, human review, safe tool use, and appropriate governance escalation.
- Strong consultative and communication skills, including the ability to explain complex technical concepts to business and technical stakeholders.
- Ability to work independently, collaborate across multidisciplinary teams, and deliver high-quality client outcomes in ambiguous environments.
- Curiosity and continuous learning mindset across AI platforms, software engineering, cloud, data, and applied machine learning.
Preferred Qualifications
- Technical depth in at least one major data platform such as Databricks or Snowflake.
- Technical depth in at least one major hyperscaler such as AWS, Azure, or GCP infrastructure and data services in addition to AI services.
- Experience using AI-assisted development tools responsibly to improve delivery productivity while maintaining engineering discipline.
- Experience building agentic workflows or AI-enabled business process automation.
- Experience with MLOps, LLMOps, CI/CD, infrastructure-as-code, automated testing, monitoring, logging, cost management, or production support.
- Experience in consulting, client delivery, technical pre-sales, solution shaping, or cross-functional product engineering environments.
- Exposure to industry-specific AI use cases in areas such as financial services, healthcare, life sciences, manufacturing, retail, public sector, or technology.
- Experience mentoring engineers, data scientists, or client teams on modern AI engineering practices.
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 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 enterprise, mid-market, and public sector organizations to improve operations, drive growth, and create value.
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 position, the targeted base salary range at the Consultant level is $106,000 - $133,000 and at the Senior Consultant level is $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]. Please note, this recipient is not able to support recruitment inquiries beyond this purpose.
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-$166K range is in the lower quartile 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 ($136K) sits 36% below the category median. Disclosed range: $106K to $166K.
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 Austin pay a median of $207,800 across 271 tracked positions. That's 3% 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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