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About This Role
JOB DESCRIPTION OVERVIEW
CREO’s AI Enablement practice helps life sciences, healthcare, and technology organizations unlock the value of AI through strategy, governance, adoption change management, and hands-on training. As a Consultant on this team, you will own client engagements end to end — leading teams to stand up AI Champions programs, launch workshop series, operationalize governance frameworks, and drive measurable adoption at scale.
This role blends deep AI Enablement expertise with the autonomy, leadership, and accountability of a seasoned consultant. You will serve as a trusted advisor to client-side Chief AI Officers, or those delegated to lead AI, and their champion communities, mentor junior staff, shape delivery strategy, and actively grow the offering through business development. You live the CREO values, and you are always learning and always teaching.
POSITION RESPONSIBILITIES
Lead and Deliver AI Enablement Engagements
You will own client AI programs across the full lifecycle — from discovery and planning through delivery, adoption, and ongoing program leadership.
- Lead and manage client engagements focused on AI readiness assessments, governance frameworks, champion programs, workshop series, and use-case discovery, with significant autonomy over scope, approach, and outcomes.
- Oversee the end-to-end lifecycle of AI enablement projects, from initial assessment and requirements gathering through implementation, adoption, and post-deployment support.
- Direct discovery and working sessions to identify and assess AI use cases based on business value, feasibility, risk, and adoption readiness, translating client pain points into practical opportunities such as chatbots, workflow automations, knowledge management tools, and analytics assistants.
- Manage multiple concurrent workstreams and engagement teams, tracking scope, schedule, deliverables, risks, and action items, and serving as the delegated Project Manager where required.
- Act as a trusted advisor and right hand to client-side AI leaders, program leads, and champion communities — driving decisions, capturing outcomes, and sustaining momentum between sessions.
- Collaborate with AI engineers and solution architects to translate technical work into narratives that land with executives and end users, and partner with clients and technical teams to deploy specialized AI or machine learning tools into client environments when needed.
- Support AI solutions in regulated or compliance-sensitive environments by documenting requirements, risks, controls, and user guidance to the quality standard clients expect.
- Mentor and develop junior staff, ensuring engagements meet client expectations, are completed on time, and align with industry best practices and emerging technologies.
- Maintain personal billable utilization at the rate of 1700 hours per year.
Develop AI-Focused Client Deliverables
You will produce polished, insight-rich artifacts that make complex AI concepts accessible and actionable for client leadership and end users.
- Build client-ready PowerPoint decks, workshop materials, playbooks, and written reports that communicate complex AI concepts clearly and compellingly to non-technical audiences.
- Develop AI strategy documents, adoption roadmaps, solution architectures, and financial models that articulate business value and demonstrate measurable impact.
- Produce governance artifacts including AI solution intake forms, risk models, use-case inventories, and executive-ready status reports with strong attention to quality and client-readiness.
- Develop and maintain enablement assets such as dashboards, SharePoint sites, training repositories, champion community hubs, prompt libraries, and intranet content that sustain adoption after each engagement.
Facilitate AI Upskilling, Training, and Champion Programs
You will help non-technical users understand, adopt, and get lasting value from AI tools and solutions.
- Design and facilitate CREO’s AI Foundations workshops, champion kick-offs, hands-on labs, case contests, and recurring office hours for client teams.
- Lead AI adoption change management — internal comms, awareness campaigns, and community-building — that turns pilots into organization-wide deployments.
- Coordinate training logistics and attendee experience across in-person and remote modalities, ensuring every session translates into adoptable use cases.
- Package lessons learned, reusable templates, and engagement materials into scalable assets for future CREO AI Enablement work.
Drive AI Tool Fluency — Always Learning, Always Teaching
AI is moving fast. You will be expected to stay current and bring what you learn back to clients and colleagues.
- Continuously evaluate new AI tools, platforms, and agent frameworks, staying current on emerging capabilities relevant to client work.
- Actively use AI in your own daily work — drafting, summarization, research, deck development, and analysis — demonstrating practical value through what you produce.
- Share what you learn through internal knowledge shares, client office hours, and champion enablement sessions, helping the rest of the CREO team and our clients get sharper, faster.
Contribute to Business Development
You will help grow the AI Enablement offering and position CREO as a trusted partner and thought leader.
- Achieve an annual sales target, as communicated separately.
- Identify AI-driven opportunities within existing client relationships and help acquire new clients, conducting AI readiness assessments and preparing proposals for enablement engagements.
- Foster relationships with senior executives and champion communities, influencing decision-makers to drive revenue growth and expand CREO’s AI consulting offerings.
Communicate with Clarity and Executive Presence
Strong communication is non-negotiable. You will regularly translate complex work for non-technical audiences and sustain momentum across client teams.
- Record and summarize technical and executive discussions during client and internal meetings, ensuring decisions and action items are captured and circulated to the right stakeholders.
- Translate stakeholder interviews, use-case discovery sessions, and workshop outputs into structured written artifacts the client can act on.
- Communicate clearly, concisely, and confidently in both written and verbal form across technical and non-technical audiences, up to and including executive leadership.
- Exhibit communication and collaboration best practices including which tool when and adjusting messaging to the audience.
REQUIRED SKILLS & QUALIFICATIONS
We hire for expertise, leadership, and the drive to bring others along. If you meet most of these qualifications and are excited about this work, we encourage you to apply.* 5+ years of consulting experience or equivalent work experience, including leading engagements and mentoring junior staff.
- Bachelor’s degree in a relevant field; master’s or equivalent post-graduate degree (or in process) preferred.
- Proven ability to design and deliver AI-enabled solutions — tools, automations, chatbots, workflow assistants, analytics tools, or internal productivity applications — that drive adoption and business value.
- Demonstrated leadership managing project teams and client engagements with significant autonomy and accountability for outcomes.
- Exceptional verbal and written communication skills; attention to detail; able to explain AI concepts clearly to non-technical audiences and present to senior-level stakeholders without sacrificing accuracy.
- Advanced expertise in PowerPoint, Word, Excel, and Outlook, with a portfolio of polished, client-ready deliverables, models, roadmaps, and presentations.
- Deep working fluency with the Microsoft 365 suite (Teams, SharePoint, OneDrive, Planner, OneNote).
- Hands-on experience with generative AI tools (Copilot, Azure Foundry, OpenAI, Anthropic, etc.) and a genuine desire to adopt and teach new AI tools as they emerge.
- Experience working with cloud environments such as Microsoft Azure and AWS to support AI application deployment, user management, provisioning, and automation tasks.
- Proven ability to manage multiple concurrent workstreams on tight deadlines with shifting priorities.
- Experience contributing to business development, including proposal writing, client relationship management, and identifying new service opportunities.
- Commitment to CREO’s core values and making a meaningful difference in the world.
PREFERRED QUALIFICATIONS* Experience in Project, program or change management; PMP or Prosci (in progress or completed) a plus.
- Experience leading or delivering technical training, or within a Learning and Development function or practice.
- Experience designing, leading, or facilitating training workshops or enablement programs.
- Exposure to life sciences, biotech, healthcare, or other regulated industries.
- Experience with or knowledge of AI or machine learning tools such as AlphaFold, Chai-1, language models, and NLP workflows.
- Hands-on experience building SharePoint sites, Power Apps, or Power Automate flows.
- Familiarity with AI governance concepts: responsible AI, risk frameworks, and enterprise AI policies.
- Experience supporting champion communities, internal enablement groups, or AI adoption programs.
- Relevant AI certifications such as Generative AI for Everyone (DeepLearning.AI), Microsoft AI Fundamentals, etc.
Please note: This application may be reviewed in part by automated systems to help identify qualified candidates.
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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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At creo, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
creo AI Hiring
creo has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Raleigh, NC, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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