Interested in this AI/ML Engineer role at Thought Logic Consulting?
Apply Now →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.
The Role
We are looking for an analytically strong and motivated Consultant with 2–3 years of experience (or a recent MS/MBA graduate with a concentration in Data, Technology, AI, or a related field) to join our growing Data \& AI practice. In this role, you will support senior consultants on client engagements by conducting research and benchmarking, documenting stakeholder interviews, developing strategy and governance artifacts and deliverables, and helping clients build the operational foundations for responsible AI adoption at scale. You will gain hands\-on exposure to enterprise AI governance, operating model design, and cross\-functional stakeholder engagement while building your consulting skills in a collaborative, client\-facing environment.
What You'll Do
- Support current\-state discovery by conducting research and benchmarking to assess AI maturity and industry positioning
- Support intake and prioritization as well as the development of frameworks to facilitate it through data gathering and analysis
- Synthesize findings into structured insights, reports, and recommendations
- Develop executive\-ready deliverables including process maps, workflow diagrams, RACIs, roadmaps, presentations, and summaries
- Support stakeholder alignment across enterprise architecture, legal, privacy, information security, and business teams
- Build and maintain inventories of AI tools, models, data assets, and vendors
- Draft governance policies, standards, and risk classification templates, including guidance on tool usage
- Partner with engineering teams to support documentation of monitoring approaches, model performance tracking, and risk controls
- Participate in governance forums, documenting decisions and tracking action items
- Coordinate stakeholder interviews and document key insights and requirements
- Assist in development of training materials and adoption resources
- Support cross\-functional collaboration across legal, risk, compliance, and technology teams
### Who You'll Work With
- Senior strategists and engagement leads who will mentor you and provide hands\-on guidance across client engagements
- Clients across industries including financial services, energy and utilities, retail, healthcare, and technology who are building AI governance capabilities
- Cross\-functional stakeholders spanning technology, legal, risk, compliance, HR, and business operations
- Colleagues who prioritize curiosity, humility, collaboration, and hands\-on problem\-solving
- A supportive environment where ongoing learning and mentorship are central to your professional growth
### What You'll Bring
- 2–3 years of professional experience in consulting, data analytics, AI/technology strategy, governance, or a related analytical role; OR a recently completed MS or MBA with a concentration in Data Science, Technology, AI, Industrial Engineering, or a related field
- Strong understanding of data and AI fundamentals including familiarity with Generative AI, Agentic AI, machine learning concepts, data governance, and responsible AI principles
- Excellent analytical and research skills with the ability to synthesize complex information into clear, structured deliverables
- Strong written and verbal communication skills, including the ability to create polished documents, presentations, and process visualizations
- Ability to work collaboratively across teams, build rapport with stakeholders at various levels, and contribute to alignment and conflict resolution in cross\-functional settings
- Strong problem\-solving orientation with comfort navigating ambiguity and managing multiple workstreams simultaneously
- Proficiency in Microsoft Office (PowerPoint, Excel, Word) and familiarity with collaboration and visualization tools
- Bachelor’s degree in Business, Technology, Data Science, Engineering, Industrial Engineering, or a related field
Bonus Points If You Have* Previous consulting firm experience or internship in a strategy, governance, or analytics capacity
- Exposure to AI governance frameworks, data governance programs, or enterprise operating model design
- Familiarity with data and AI platforms such as Snowflake, Databricks, or Palantir Foundry
- Background in Industrial Engineering, operations research, or process optimization methodologies
- Experience with process mapping tools, data visualization, or business intelligence platforms
- Relevant certifications in data, AI, cloud platforms, or project management
### 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
- Direct access to firm leadership and influence over how we grow and evolve
- 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
- The opportunity to flex in a continuous learner environment. Get access and exposure to the latest tools, technologies and trends in the AI space
### 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $194,400.
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
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.