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About This Role
General Information
Locations
- Nashville, TN
Business Function Data & analytics Date posted Thursday, September 3, 2026 Ref # JBR0001660 Description and Requirements
Job Description Data & AI Go-to-Market Lead – Director or Sr. Principal - South Central Region (Nashville, TN)
*This role is**noteligiblefor100% remotework. Employees must live within a commutable distance of the Nashville, TN 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 around 25%.***
Who You'll Work With
At Slalom, we combine personal connection with global scale to create environments where work is loved and life is enriched. Our mission is to help organizations redefine possibilities, shape the future, and achieve transformative goals. This position reports to the Regional Capability Lead and is based in Nashville, TN supporting the South Central Region (Nashville, Jackson, Knoxville, Louisville, and Memphis), where you'll play a pivotal role in driving Data and AI sales and fostering customer-centric go-to-market strategies.
As the Data & AI Go-to-Market Lead for the South Central Region, you will be responsible for technical leadership focused on solution architecture, technical selling, and GTM execution. You will own the technical vision, pre-sales strategy, and solution design for the region’s most strategic data, analytics, and AI opportunities. You will partner closely with sales, client partners, delivery leaders, and technology partners to architect differentiated solutions, lead executive-level technical conversations, and drive pipeline growth through consultative technical selling.
What You’ll Bring
- Prior Consulting experience with Minimum 5+ years in Managed or IT Professional Consulting required.
- 10+ years of experience in data, analytics, cloud, and AI solution architecture, preferably within consulting or professional services.
Technical Selling & GTM Expertise
- Ability to translate business outcomes into technical architectures and roadmaps that resonate with executive and engineering stakeholders.
- Track record of supporting annual revenue targets through technical leadership in pursuits.
- Strong background in technical pre-sales, solution consulting, or sales engineering, leading complex pursuits from discovery through solutioning and close.
- Proven success serving as Lead Solution Architect or Technical SME in large, multi-capability deals.
- Proven ability to architect end-to-end enterprise data platforms and AI solutions, spanning ingestion, transformation, analytics, ML, governance, and consumption layers.
- Experience across modern data and AI ecosystems, including: + Snowflake, Databricks, AWS, Microsoft Azure, Google Cloud + OpenAI, Anthropic, AI orchestration frameworks, ML platforms + Dbt, Fivetran, Matillion, Dataiku, and related ETL technologies
Executive Presence & Thought Leadership
- Comfort engaging with CXOs and senior technology leaders in strategic, consultative conversations.
- Experience presenting at industry events, conferences, and executive forums as a data & AI thought leader.
- Strong storytelling skills—able to articulate complex architectures in clear, compelling business terms.
What You’ll Do
Go-to-Market & Partner Collaboration
- Define and evolve regional solution offerings and reference architectures aligned with Slalom’s national Data & AI strategy.
- Partner deeply with Snowflake, Databricks, AWS, Microsoft, Google, OpenAI, and Anthropic to co-create joint offerings and GTM motions.
- Represent Slalom as a technical thought leader at conferences, executive briefings, and industry events.
Business Development & Revenue Ownership
- Be accountable for technical contribution to sales growth, pipeline creation, and deal conversion.
- Support RFPs, RFIs, POCs, and workshops through solution design, architecture definition, and value articulation.
- Shape multi-capability solutions, integrating data, cloud, AI, digital, and industry-specific offerings into cohesive architectures.
Team & Technical Capability Development
- Build and mentor a high-performing team of solution architects and technical GTM leaders.
- Establish architectural standards, solution frameworks, and reusable assets.
- Coach consultants and architects on technical selling, storytelling, and executive communication.
Delivery Engagement
- Contribute in billable roles as a Solution Architect or Technical Lead on strategic accounts.
- Ensure architectural quality, governance, and long-term platform sustainability.
Solution Architecture & Technical Strategy
- Own the regional technical vision and architecture strategy for Data & AI solutions.
- Design and lead end-to-end solution architectures across modern data platforms, analytics, and AI initiatives.
- Act as the technical authority and escalation point for complex pursuits, proposals, and client engagements.
- Partner with delivery teams to ensure architectural integrity, scalability, security, and performance from sales through execution.
Technical Selling & Pre-Sales Leadership
- Serve as Lead Solution Architect and Technical Seller on strategic pursuits, shaping solution design, value propositions, and business cases.
- Lead executive discovery sessions, architecture workshops, whiteboarding sessions, and solution demonstrations.
- Drive consultative selling motions, aligning technical architecture to measurable business outcomes.
- Partner closely with sales and client partners to build and convert pipeline through technical credibility and solution excellence.
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 position at the Director level base salary range is $215,000 to $262,000 and the Senior Principal level base salary range is $175,000 to $215,000.
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 an accommodation during the interview process.
Salary Context
This $175K-$215K 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $175K to $215K.
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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