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About This Role
### Description
Thought Logic Consulting is a functionally\-led, digitally enabled consultancy that exists at the intersection of business transformation and technology innovation. We partner with clients to solve their most complex business problems and we do it in a highly collaborative, local\-market approach, giving clients senior\-level attention and giving our consultants room to grow, lead, and build.
We're seeking an experienced leader to support our growing Data \& AI practice. This will be a strategic operator who can transform market opportunity into revenue growth. Reporting directly to the overall Managing Partner of our D\&A Practice, you'll own our go\-to\-market engine, driving sales strategy, solutioning excellence, and revenue generation.
This isn't a staff role, it's a builder position. You'll architect our growth strategy, cultivate high\-value client relationships, and position our practice as the trusted partner for data\-driven transformation.### What You'll Own
Revenue Growth \& Pipeline Management* Drive $5M\+ in annual sales with accountability for pipeline from identification through closure
- Lead complex pursuits with deal sizes ranging from $500K to $5M\+
- Partner with client executives and account teams to identify and develop opportunities
- Track, forecast, and report on sales metrics and conversion rates
Go\-to\-Market Strategy \& Solutioning* Translate practice vision into executable market strategies and compelling value propositions
- Serve as Solution Lead on strategic pursuits, architecting innovative approaches that win
- Develop sales playbooks, pitch materials, and reusable IP that differentiate our offerings
- Build strategic partnerships with technology vendors (Snowflake, Databricks, AWS, Microsoft, OpenAI/Anthropic)
- Lead RFPs, workshops, and proof\-of\-concept initiatives
Market Presence \& Thought Leadership* Represent the practice through speaking engagements, conferences, and industry events
- Cultivate relationships with client CXOs, particularly Chief Data Officers and CTOs
- Lead client\-facing events, workshops, and executive briefings
- Build the practice's brand through strategic marketing and content development
Team Development* Build and mentor a go\-to\-market team
- Partner with delivery leaders to ensure alignment between what we sell and what we deliver
- Foster a culture of excellence, accountability, and continuous improvement
Delivery Contribution* Maintain client\-facing credibility through selective delivery engagement (20\-40% billable)
- Serve in advisory or solution architecture roles on strategic accounts
### What You'll Bring
- 10\+ years in consulting, with at least 5 years in data \& analytics
- Proven track record managing $6M\-$10M\+ in practice revenue
- Direct experience leading complex B2B sales cycles in professional services
- History of building or scaling a go\-to\-market function
Sales Excellence* Demonstrated ability to create pipeline, qualify opportunities, and close deals
- Track record of meeting or exceeding revenue targets in competitive markets
- Skilled at navigating complex stakeholder landscapes and building executive relationship.
Technical Credibility* Strong fluency in data \& analytics technology ecosystem (cloud platforms, AI/ML, modern data tools)
- Ability to architect solutions and speak credibly with technical buyers
- Understanding of modern data architecture patterns and AI/ML applications
Leadership* Entrepreneurial mindset with bias toward action
- Exceptional communication and presentation skills at all levels
- Strategic thinker who can also execute
### Why Thought Logic?
- Work on transformations that matter, not slide decks that sit on shelves
- Real responsibility and ownership over how work gets delivered and how clients experience us
- Opportunity to shape and grow a practice but also have a say in the strategic direction of the company
- A culture that values depth over optics, outcomes over activity, and people over process
- The chance to grow your career in a firm that's scaling thoughtfully and intentionally, not just chasing growth for growth's sake
### About Thought Logic Consulting
At Thought Logic, we believe consulting should work differently. Since our founding, we’ve been on a mission to challenge the expected by bringing together the strategic depth of Big 4 experience with the personalized attention and agility you won’t find at traditional firms. We’re a functionally\-led, digitally enabled consultancy that exists at the intersection of business transformation and technology innovation. What sets us apart isn’t just what we do—it’s how we do it. We work side by side with Fortune 500 teams as player\-coaches, building lasting capability while delivering measurable outcomes. From our roots in Atlanta to our delivery across the country, we’ve grown by staying true to one simple principle: our clients’ success is our success.
Thought Logic is an equal opportunity employer. We prohibit discrimination and harassment of any kind and provide equal employment opportunities without regard to: race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law. Thought Logic conforms to both the spirit and letter of all applicable employment laws and regulations.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Thought Logic 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Thought Logic Consulting AI Hiring
Thought Logic Consulting has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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