Interested in this AI/ML Engineer role at Precision AQ?
Apply Now →About This Role
We are seeking a Manager, AI Enablement to support the adoption of AI across a portfolio of three marketing agencies. This role focuses on identifying, organizing, and helping implement practical AI use cases across both client\-facing (billable) and back\-office teams. You will help translate enterprise AI strategy into real workflow improvements, productivity gains, and measurable business impact.
This individual will work closely with business leaders, the AI transformation team, and IT partners to embed AI into day\-to\-day workflows and operational processes.
Duties
Support AI Adoption and Enablement Across Teams
- Partner with agency and functional leaders to identify opportunities to integrate AI into workflows
- Support development and execution of AI training and enablement programs
- Help create role\-specific playbooks and best practices
- Coordinate AI workshops, office hours, and internal enablement sessions
- Support development of an internal network of AI champions
Manage Bottoms\-Up AI Use Case Pipeline
- Collect and organize AI use cases from teams across agencies and functions
- Maintain a structured intake and tracking system for use cases
- Categorize opportunities based on impact, feasibility, and alignment with priorities
- Partner with the AI Generalist and technical teams to support early\-stage validation
Support Prioritization and Leadership Reviews
- Prepare use cases for review by leadership and decision\-makers
- Summarize expected benefits, required effort, and dependencies
- Support prioritization discussions and documentation of decisions
- Ensure alignment with broader AI initiatives and priorities
Assist with Workflow Integration and Pilot Execution
- Work with teams to integrate AI into existing workflows
- Support pilot execution and capture feedback
- Document successful use cases and contribute to scaling efforts
- Help standardize repeatable approaches across teams
Capture and Organize Client AI Signals
- Gather AI\-related requests, needs, and trends from client\-facing teams
- Organize and synthesize inputs across agencies
- Share insights with AI leadership to inform priorities and capability development
Track Productivity Impact and Support Reporting
- Help track time savings and efficiency improvements from AI use cases
- Support development of reporting on productivity and adoption
- Maintain visibility into use case pipeline progress and outcomes
Coordinate Across AI, IT, and Business Teams
- Work closely with the AI transformation team and Senior Director
- Partner with IT and architecture teams to align on feasibility and scaling
- Help ensure use cases follow the defined AI lifecycle
Minimum Education \& Experience:
- Bachelors Degree Required
- 4–6\+ years of experience in consulting, operations, project management, or a related field.
- Experience working in cross\-functional or matrixed environments and strong organizational skills.
- Ability to explain AI\-supported thinking and outputs clearly to non\-technical stakeholders
- Strong communication and stakeholder management
- Structured problem\-solving and organization
- Ability to translate business needs into actionable tasks
- Curiosity and interest in AI and emerging technologies
- Ability to operate in a fast\-paced, evolving environment
Preferred:
- Experience in a marketing agency, professional services, or similar environment.
- Hands\-on working knowledge of generative AI tools used in business settings (such as Copilot, ChatGPT, or similar) to support writing, analysis, synthesis, or decision making.
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Precision Medicine Group is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, age, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status or other characteristics protected by law.
If you are an individual with a disability and require a reasonable accommodation to complete any part of the application process or are limited in the ability or unable to access or use this online application process and need an alternative method for applying, you may contact Precision Medicine Group at [email protected].
It has come to our attention that some individuals or organizations are reaching out to job seekers and posing as potential employers presenting enticing employment offers. We want to emphasize that these offers are not associated with our company and may be fraudulent in nature. Please note that our organization will not extend a job offer without prior communication with our recruiting team, hiring managers and a formal interview process.
Salary Context
This $100K-$151K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Precision AQ, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($125K) sits 31% below the category median. Disclosed range: $100K to $151K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Precision AQ AI Hiring
Precision AQ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $151K - $151K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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