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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 change management, hands\-on upskilling, and development. As a Senior Analyst, you will play a pivotal role leading and delivering AI enablement engagements, working directly with client stakeholders to design, build, and scale AI solutions. You will take ownership of workstreams, lead technical delivery, and help drive AI strategy and adoption across client organizations.
This role is ideal for a technically strong, hands\-on practitioner who is as comfortable building AI agents as they are guiding executive and non\-technical stakeholders on how and why to use them. 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.** Lead the design, build, test, and deployment of AI agents, assistants, and workflow automations aligned to client business objectives using platforms such as Microsoft Copilot Agent Builder, Azure AI Foundry, Azure OpenAI, and the Anthropic API.
- Own the end\-to\-end lifecycle of AI solutions, from requirements and architecture through deployment and optimization
- Comfortable working in regulated or compliance\-sensitive environments with appropriate documentation and oversight.
- Navigate technical solutioning discussions with client and internal stakeholders; translate requirements into scalable AI architectures.
- Assess, classify, and document AI solutions within client governance frameworks; guide AI intake, risk documentation, and compliance\-sensitive oversight processes.
- Ensure solutions are built for reuse and scalability by packaging lessons learned, reusable prompts, agent templates, and solution blueprints into CREO assets.
- Working 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.
AI Process Engineering
*Analyzing current workflows, documenting business operations, and collaboratively reengineering processes to incorporate advanced technologies such as AI, process automation, and analytics, you will partner with stakeholders across business and technology functions to ensure that redesigned workflows are efficient, scalable, and aligned with organizational goals.** Analyze, document, and map current\-state (as\-is) business processes, identifying pain points, inefficiencies, and opportunities for automation and optimization.
- Design future\-state (to\-be) processes that integrate AI capabilities, workflow automation, and decision support.
- Conduct stakeholder interviews and workshops to gather requirements and build consensus across cross\-functional teams.
- Collaborate with solution architects, developers, and subject matter experts to translate process requirements into actionable technical designs.
- Develop detailed documentation including process maps, gap analyses, functional requirements, and operating procedures to guide solution implementation.
- Ensure process designs align with regulatory and compliance requirements specific to the life sciences and healthcare industries.
- Support change management efforts, including user training and adoption activities, to ensure successful implementation of redesigned processes.
- Monitor and evaluate implemented solutions for effectiveness and recommend improvements based on performance metrics and stakeholder feedback.
Support AI Enablement Workstreams
*You will contribute across the full lifecycle of client AI programs, from discovery through deployment and adoption.** Assist client scoped AI enablement workstreams or engagements, ensuring delivery quality, timelines, client satisfaction, and alignment with business outcomes and ensuring technical requirements, design and legitimacy.
- Translate client pain points and workflow friction into strategic, practical AI opportunities such as 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.
- 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 of 1700 hours per year.
Drive AI Adoption, Training, and Continuous Improvement
*You will help clients and internal teams understand, adopt, and get practical value from AI solutions while staying current in a fast\-evolving AI landscape.** Present and train non\-technical audiences on how to use AI solutions, including agents, assistants, and Copilot\-based tools, in a clear, practical, and confidence\-building way.
- Build training materials, onboarding resources, user guides, prompt libraries, and supporting documentation that explain how AI solutions work and the business problems they are designed to solve.
- Support AI adoption and change management efforts, including workshops, office hours, internal communications, awareness campaigns, and champion enablement sessions.
- Communicate clearly with technical and non\-technical stakeholders, translating requirements, workshop outputs, and technical concepts into structured, actionable client\-facing materials.
- Stay current on emerging AI tools, platforms, and agent frameworks, and apply that learning to improve client solutions, internal delivery assets, and team knowledge sharing.
- 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.
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.** 2–4\+ years of relevant experience in technical consulting, AI engineering, professional services, business process improvement, business analysis, or systems implementation, preferably within healthcare, life sciences, or another regulated industry.
- Hands\-on experience building AI agents, assistants, workflow automations, or AI\-enabled solutions using platforms such as Azure OpenAI, Azure AI Foundry, Microsoft Copilot Studio, Google Vertex AI, Google Agent Builder, Anthropic, OpenAI, AWS Bedrock, or comparable frameworks.
- Proficiency in Python for scripting, data processing, and API integration; familiarity with JavaScript or PowerShell is a plus.
- Experience working with APIs, knowledge bases, and structured or 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.
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)
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
creo AI Hiring
creo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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