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About This Role
JOB DESCRIPTION OVERVIEW
CREO’s AI Enablement team helps life sciences, healthcare, and technology organizations unlock the value of AI through strategy, governance, adoption of change management, hands-on upskilling, and development. As a Senior Analyst on this team, you will work alongside senior AI Enablement leadership to support client engagements that stand up AI Champions programs, launch workshop series, configure and prototype AI agents and assistants, operationalize governance frameworks, and support user adoption.
This role is ideal for a technically strong, hands-on practitioner who is as comfortable building AI agents as they are walking a non-technical audience through how and why to use one. The right candidate is deeply curious, adapts quickly in a fast-moving AI landscape, and brings the communication skills to translate technical possibilities into practical business outcomes — from the executive conference room to a one-on-one coaching session with an end user.
Experience working in health and life sciences is a plus.
POSITION RESPONSIBILITIES
Build and Deploy AI Solutions
This is the technical core of the role. You will design, build, test, and deploy AI-powered solutions aligned to client business requirements.
- Design, configure, prototype, test, and support AI agents, assistants, and workflow automations using platforms such as Microsoft Copilot Agent Builder, Azure AI Foundry, Azure OpenAI, and the Anthropic API, while assisting production architecture and deployment led by senior engineers and client technical teams.
- Comfortable working in regulated or compliance-sensitive environments with appropriate documentation and oversight.
- Foundational knowledge of agentic AI concepts including RAG, tool use, and knowledge base configuration; exposure to multi-agent architectures a plus.
- Convert requirements from AI program leads, champions, cloud teams, and solution architects into functional AI solutions; generate supporting AI solution proposal documentation.
- Assess, classify, and document AI solutions according to client governance frameworks; support AI intake processes and risk documentation for regulated environments.
- Package lessons learned, reusable prompts, agent templates, and solution blueprints into scalable assets for future CREO engagements.
Support AI Enablement Engagements
You will contribute across the full lifecycle of client AI programs, from discovery through deployment and adoption.* Support and participate in stakeholder interviews, discovery sessions, and working sessions to identify, assess, and prioritize AI use cases based on business value, feasibility, risk, and adoption readiness.
- Translate client pain points into practical AI opportunities, including chatbots, workflow automations, knowledge management tools, analytics assistants, onboarding tools, and operational support applications.
- Work 1:1 with business users to understand their workflows, identify friction points, and prototype or refine AI solutions that fit their day-to-day reality.
- Draft requirements documents for AI prototypes and solutions, capturing functional specifications, data inputs, governance considerations, and user acceptance criteria.
- Draft design artifacts and work instructions for AI solutions, including user guides, prompt libraries, configuration notes, and maintenance documentation.
- Serve as a right hand to client-side AI leaders, program leads, and champion communities — coordinating meetings, capturing decisions, and keeping momentum between sessions.
- Comfortable managing personal workload across multiple concurrent engagements; able to prioritize tasks, meet deadlines, and proactively communicate status and progress to the team.
- Partner with senior AI Enablement consultants, AI engineers, and client leaders to deliver AI strategy, enablement, and implementation support across client engagements.
- Maintain personal billable utilization at the rate of 1700 hours per year
Facilitate AI Upskilling and Training
You will play an active role in helping non-technical users understand, adopt and get value from new AI solutions.* Present and train non-technical audiences on how to use and update AI solutions — including agents, assistants, and Copilot-based tools in ways that are clear, practical, and confidence-building.
- Build training recordings and onboarding materials that explain not just how to use a new AI solution, but why it was designed the way it was, and what problems it is meant to solve.
- Participate confidently in client meetings as a knowledgeable AI practitioner — able to speak clearly and credibly about AI tools, capabilities, and how they apply to client needs.
- Support the co-design and co-facilitation of CREO’s AI Foundations workshops, champion kick-offs, hands-on labs, and recurring office hours. Facilitation experience is a plus; comfort in front of a room is essential.
- Support AI adoption change management including internal communications, awareness campaigns, and community-building that turns pilots into organization-wide deployments.
Drive AI Tool Fluency and Teach Back
AI is moving fast. You will be expected to stay current and bring what you learn back to clients and colleagues.* Continuously explore and evaluate new AI tools, platforms, and agent frameworks; stay current on emerging capabilities relevant to client work.
- Use AI in your own daily work: drafting, summarization, research, analysis, and solution development. Demonstrate practical value through what you produce.
- Share what you learn through internal knowledge shares, client office hours, and champion enablement sessions; help the rest of the CREO team and our clients get sharper, faster.
- Approach the fast pace of AI change with genuine curiosity, a willingness to pick up new tools, frameworks, and methods quickly, and to learn from engineers, facilitators, and clients alike.
Communicate with Clarity and Confidence
Strong communication is non-negotiable in this role. You will regularly translate technical work for non-technical audiences.* Communicate clearly and confidently in both written and verbal form across technical and non-technical audiences; participate in engagements with executive leadership under the direction of senior AI Enablement leaders.
- Translate stakeholder interviews, use-case discovery sessions, and workshop outputs into structured written artifacts the client can act on.
- Record and summarize technical and executive discussions, ensuring decisions and action items are captured and circulated to the right stakeholders
SKILLS & QUALIFICATIONS
We hire for curiosity, technical depth, and the ability to bring others along. If you meet most of these qualifications and are excited about the work, we encourage you to apply.
Required Qualifications* 1–4 years of relevant experience in a technical consulting, AI engineering, or professional services environment; or equivalent demonstrated hands-on experience building and deploying AI solutions.
- Hands-on experience building AI agents and assistants using platforms such as Microsoft Copilot Agent Builder, Azure OpenAI, Azure AI Foundry, or equivalent agentic frameworks.
- Working proficiency in Python for scripting, data processing, and API integration; familiarity with JavaScript or PowerShell a plus.
- Experience working with APIs, knowledge bases, and structured/unstructured data as inputs to AI solutions.
- Deep working fluency with the Microsoft 365 suite, including Copilot, Teams, SharePoint, OneDrive, and Copilot Studio.
- Exceptional verbal and written communication skills; able to explain AI technology clearly to non-technical audiences without sacrificing accuracy.
- Comfortable participating in stakeholder interviews and discovery sessions; able to translate pain points into structured requirements and practical AI opportunities.
- Comfortable working 1:1 with business users to understand workflows and prototype AI solutions.
- Self-directed and organized; able to manage multiple workstreams with competing priorities.
- Genuine curiosity and a demonstrated ability to learn quickly in a fast-evolving AI landscape.
- Strong proficiency in PowerPoint and Word; strong working knowledge of Excel and Outlook.
- Bachelor’s degree in a relevant field or equivalent work experience.
- Commitment to CREO’s core values and making a better, healthier world.
- Travel may be required (local and overnight when appropriate).
Preferred Qualifications
- Experience building multi-agent architectures, implementing RAG pipelines, or working with vector databases and embedding models.
- Hands-on experience building SharePoint sites, Power Apps, or Power Automate flows.
- Experience designing, coordinating, or facilitating training workshops or enablement programs; comfort presenting in front of a room is a plus.
- Exposure to life sciences, biotech, healthcare, or other regulated industries.
- Familiarity with AI governance concepts: responsible AI, risk frameworks, and enterprise AI policies.
- Project or program coordination experience.
- Relevant AI certifications such as Generative AI for Everyone (DeepLearning.AI), Microsoft AI Fundamentals, or equivalent.
- Preference given to candidates in the Raleigh-Durham, NC area or a life science hub (San Diego, Boston, San Francisco Bay, Orange County, Seattle)
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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. Senior-level AI roles across all categories have a median of $228,700.
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.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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