Interested in this AI/ML Engineer role at Ahead?
Apply Now →About This Role
AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.
At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD.
We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived.
*We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD.*
AHEAD is seeking a Principal Consultant, AI Services to help enterprise clients move from AI ambition and experimentation to measurable business impact.
In this role, you will lead high\-impact client engagements with senior business and technology executives. You will help clients identify where AI can create value, prioritize use cases, build business cases and roadmaps, design operating models and governance, and mobilize implementation with AHEAD’s broader teams of consultants, architects, engineers, and specialists.
This is a leadership role for a consulting professional who can operate at the intersection of strategy, technology, business transformation, and execution. The ideal candidate brings top\-tier consulting discipline, strong executive communication, structured problem solving, and enough AI / technology fluency to guide multidisciplinary teams — without needing to be a hands\-on ML engineer.
We are especially interested in candidates who have operated in roles equivalent to Engagement Manager, Project Leader, Senior Manager, or Director in strategy consulting, technology advisory, digital transformation, or enterprise technology environments.
### What You’ll Do
Lead client engagements from strategy through execution
- Lead complex AI and digital transformation engagements from initial diagnostic through roadmap design, implementation planning, and execution support.
- Structure ambiguous client problems into clear hypotheses, workplans, analyses, decision points, and executive\-ready deliverables.
- Manage day\-to\-day engagement delivery, including workstream planning, team leadership, synthesis, quality control, issue resolution, timelines, and client communication.
- Facilitate executive workshops and align senior stakeholders across business, technology, data, risk, and operations.
- Serve as a trusted advisor to client leaders on where AI can create measurable value and what is required to scale it responsibly.
2\. Shape AI strategy, transformation roadmaps, and business cases
- Identify and prioritize AI opportunities across productivity, growth, customer experience, decision\-making, software delivery, and operational efficiency.
- Conduct AI maturity and readiness assessments across strategy, data, platforms, governance, talent, process, and change management.
- Build practical AI roadmaps that sequence pilots, platform enablers, governance actions, operating model changes, and value realization milestones.
- Develop business cases and value\-tracking approaches that connect AI initiatives to measurable client outcomes.
- Help clients redesign workflows and operating processes to capture value from AI, automation, and agent\-assisted ways of working.
3\. Partner with technical teams to move ideas into implementation
- Translate business priorities into implementation\-ready initiatives in partnership with architects, engineers, data specialists, and security/governance experts.
- Help define pilot scope, success metrics, adoption plans, governance requirements, and scale\-up pathways.
- Ensure AI recommendations are practical, credible, and grounded in enterprise technology realities.
- Bridge communication between executive stakeholders and technical delivery teams.
4\. Lead and develop teams
- Lead multidisciplinary teams across one or more client workstreams.
- Coach consultants and specialists through structured problem solving, client communication, synthesis, and executive\-ready output.
- Create a high\-performance team environment with clear ownership, strong feedback, and a consistent quality bar.
- Help build reusable methods, tools, templates, and delivery approaches for AHEAD’s AI Services practice.
5\. Support business and practice growth
- Identify follow\-on opportunities within client engagements and help shape the path from initial advisory work to broader transformation programs.
- Contribute to proposals, solution design, client presentations, and account growth efforts.
- Help develop AHEAD’s AI offerings, points of view, go\-to\-market materials, and thought leadership.
- Stay current on enterprise AI trends, including generative AI, agentic AI, AI governance, responsible AI, AI\-enabled software delivery, data platforms, and cloud ecosystems.
### What You’ll Bring
Required qualifications
- Typically 7–12\+ years of experience in management consulting, technology advisory, digital transformation, enterprise technology, AI strategy, or related client\-facing roles.
- Proven experience leading client engagements, workstreams, or project teams with senior business and technology stakeholders.
- Strong structured problem\-solving skills, including the ability to break down ambiguous problems, develop hypotheses, conduct analysis, synthesize insights, and communicate recommendations.
- Excellent executive communication and facilitation skills, including experience creating and presenting senior\-leadership\-ready materials.
- Experience in one or more of the following areas: AI strategy, digital transformation, use case prioritization, business case development, process redesign, automation, operating model design, AI governance, or technology\-enabled transformation.
- Sufficient AI and technology fluency to work credibly with business leaders, CIO / CTO organizations, architects, engineers, data teams, and risk/governance stakeholders.
- Demonstrated ability to lead teams, coach talent, manage timelines, and maintain a high bar for client\-ready work.
Preferred qualifications
- Background in top\-tier management consulting, Big 4 advisory, technology consulting, enterprise transformation, or a similar high\-performing professional services environment.
- MBA or other advanced degree preferred.
- Experience working with AI, data, cloud, software delivery, MLOps, responsible AI, or enterprise technology platforms.
- Experience helping clients move from strategy and roadmap development into pilot delivery, implementation, adoption, and value realization.
- Prior experience supporting business development, proposal development, account growth, or practice\-building activities.
What is not required
- You do not need to be a hands\-on machine learning engineer.
- You do not need to have deep coding experience.
- You do need to be comfortable leading business and technology conversations, asking the right questions, and translating between executive priorities and technical delivery realities.
$250,000 \- $300,000 a year
*The compensation range indicated in this posting reflects the On\-Target Earnings (“OTE”) for this role, which includes a base salary and any applicable target bonus amount. This OTE range may vary based on the candidate’s relevant experience, qualifications, and geographic location.*
Why AHEAD:
Through our daily work and internal groups like Moving Women AHEAD and RISE AHEAD, we value and benefit from diversity of people, ideas, experience, and everything in between.
We fuel growth by stacking our office with top\-notch technologies in a multi\-million\-dollar lab, by encouraging cross department training and development, sponsoring certifications and credentials for continued learning.
USA Employment Benefits include:
- Medical, Dental, and Vision Insurance
- 401(k)
- Paid company holidays
- Paid time off
- Paid parental and caregiver leave
- Plus more! See benefits https://www.aheadbenefits.com/ for additional details.
Use of AI:
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, assessing responses, or to capture recordings and create transcriptions or summaries during interviews. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans.
If you would like more information about how your data is processed, please refer to the Candidate Privacy Notice or contact us at \[email protected].
You may opt\-out of the review or analysis of your application and resume by AI tools by using the General Application. Please include the role you wish to apply for in the Additional Information field. You may also choose to opt\-out of recording and transcription at any time, including after joining an interview. Candidates will not be penalized for choosing to opt\-out.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $250K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ahead, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($275K) sits 28% above the category median. Disclosed range: $250K to $300K.
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.
Ahead AI Hiring
Ahead has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, US. Compensation range: $150K - $300K.
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
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