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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 Technical Consultant, AI Services to lead the architecture, engineering, and deployment of enterprise\-grade AI solutions for our clients.
This is a senior hands\-on technical leadership role for someone who can turn ambiguous business problems into scalable, secure, production\-ready AI systems. You will work directly with client technology and business leaders to define solution architecture, guide engineering teams, make technical trade\-offs, and ensure that AI initiatives move from prototype to measurable business impact.
The ideal candidate combines software engineering depth, applied AI / ML fluency, enterprise architecture judgment, and consulting\-style client leadership. You do not need to be a pure research scientist, but you should be credible with engineers, data scientists, architects, platform teams, security stakeholders, and senior executives.
This role is well suited for candidates with backgrounds in applied AI consulting, ML engineering, AI solution architecture, technical product development, data science engineering, or advanced analytics engineering environments.
### What You’ll Do
### 1\. Architect and build enterprise AI solutions
- Lead the architecture, design, development, and deployment of enterprise\-grade AI, GenAI, agentic, automation, and ML\-enabled solutions.
- Translate ambiguous business and technical requirements into clear solution designs, architecture decisions, implementation plans, and engineering workstreams.
- Design and build AI solution patterns such as retrieval\-augmented generation, workflow orchestration, agent\-assisted processes, model integration, API\-based automation, and human\-in\-the\-loop review.
- Make practical architecture decisions across models, data pipelines, APIs, orchestration layers, vector stores, enterprise applications, security controls, and deployment environments.
- Ensure solutions are scalable, secure, maintainable, observable, and aligned to measurable client outcomes.
### 2\. Lead technical delivery across client engagements
- Lead technical workstreams across one or more client engagements, including estimation, planning, design, build, testing, deployment, risk management, and issue resolution.
- Serve as the technical authority for project teams, owning solution quality, engineering standards, and technical decision\-making.
- Partner with client engineering, data, cloud, security, and platform teams to integrate AI solutions into enterprise environments.
- Lead technical workshops, architecture sessions, demos, design reviews, and working sessions with both technical and non\-technical stakeholders.
- Communicate complex technical concepts clearly to senior business and technology leaders.
### 3\. Establish production\-grade AI engineering standards
- Define and apply strong engineering practices across code quality, automated testing, CI/CD, observability, monitoring, reliability, scalability, security, and maintainability.
- Establish practical patterns for LLMOps / MLOps, model integration, prompt and workflow management, evaluation, guardrails, performance monitoring, and responsible AI usage.
- Design AI systems with appropriate controls for privacy, security, governance, compliance, auditability, and human oversight.
- Build and improve reusable components, reference architectures, deployment patterns, and accelerators that strengthen AHEAD’s AI delivery capability.
- Ensure pilots are built with a credible path to production and scale, not as isolated demos.
### 4\. Partner across strategy, business, and technical teams
- Work with strategy consultants, solution managers, architects, engineers, and client stakeholders to connect business priorities with technical execution.
- Help clients assess trade\-offs across speed, cost, risk, usability, accuracy, reliability, and long\-term maintainability.
- Shape technical roadmaps that sequence pilots, platform enablers, integration work, governance requirements, and scale\-up activities.
- Help define success metrics for AI solutions, including business impact, adoption, model/application quality, reliability, and operational performance.
- Act as a bridge between executive ambition and engineering reality.
### 5\. Mentor teams and build the AI Services practice
- Coach engineers, consultants, and technical specialists on solution design, engineering quality, client communication, and delivery excellence.
- Review technical designs and code to ensure high\-quality, maintainable, production\-ready output.
- Contribute to AHEAD’s AI offerings, technical methods, architecture standards, accelerators, and thought leadership.
- Support pre\-sales and solution shaping by helping define technical scope, delivery approach, effort estimates, risks, and implementation plans.
- Help elevate AHEAD’s reputation as a firm that can not only advise on AI, but build and scale it in enterprise environments.
### What You’ll Bring
### Required qualifications
- Typically 7–12\+ years of experience in software engineering, ML engineering, applied AI, data science engineering, AI solution architecture, technical consulting, or enterprise technology delivery.
- Strong hands\-on engineering experience, especially with Python, APIs, cloud\-native development, data integration, workflow automation, and enterprise system integration.
- Experience designing, building, and deploying production\-grade AI, GenAI, ML, automation, or advanced analytics solutions.
- Practical familiarity with AI solution patterns such as RAG, LLM application design, agentic workflows, orchestration, model integration, vector databases, evaluation, guardrails, and observability.
- Strong understanding of modern engineering practices, including CI/CD, automated testing, version control, containerization, monitoring, reliability, security, and scalable deployment.
- Ability to lead technical teams, review designs and code, mentor engineers, and drive delivery quality across complex workstreams.
- Strong client\-facing communication skills, including the ability to explain technical trade\-offs clearly to engineering teams, executives, and non\-technical stakeholders.
- Ability to operate in ambiguous environments, structure technical problems, make sound architecture decisions, and guide teams toward practical outcomes.
### Preferred qualifications
- Experience in applied AI consulting, advanced analytics consulting, ML engineering, AI product development, or enterprise AI platform delivery.
- Background from a high\-performing consulting, technology, AI, cloud, data, or software engineering organization.
- Experience with cloud and data platforms such as Azure, AWS, GCP, Databricks, Snowflake, Kubernetes, or similar enterprise platforms.
- Experience with LLM frameworks, orchestration tools, vector databases, model serving, ML platforms, or AI governance tooling.
- Experience moving AI solutions from prototype or pilot into production environments.
- Experience supporting technical pre\-sales, solution shaping, architecture proposals, or executive\-level technical advisory.
- Advanced degree in computer science, engineering, data science, applied mathematics, or a related field is a plus.
### What is not required
- You do not need to be a pure AI researcher.
- You do not need to have deep academic ML specialization.
- You do not need to be only a platform architect or only a data scientist.
- You do need to be a strong technical builder and leader who can design, guide, and deliver enterprise\-grade AI solutions.
$230,000 \- $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 $230K-$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 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($265K) sits 23% above the category median. Disclosed range: $230K 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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