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About This Role
Provectus is the consulting partner helping enterprise companies move from "we are exploring AI" to "AI is how we work." The AI Enablement Lead owns the curriculum that makes that shift stick: live sessions, skill\-authoring labs, executive coaching, train\-the\-trainer programs, and everything in between.
This is not an instructional role. It is a facilitation role inside a consulting engagement. The person in this seat is often the first Provectus face a customer's leadership team meets. The bar is consulting\-grade presence plus the genuine appetite to teach what they learn, in rooms that push back.
You are building the playbook as you run it. Every customer engagement teaches you something. You feed that back into the curriculum. The offering matures because you make it mature.
We will teach Cowork and the underlying AI mechanics. What we cannot teach is teaching itself, the love of standing in front of a room, or the discipline of preparing for a session you have given fifty times as if it were your first.
### What you will own:
1\. Delivery across the full enablement curriculum:
- The 2\-hour Cowork 101 baseline, co\-developed with Anthropic
- Role\-specific tracks: Legal, Finance, IT, Sales, and custom tracks as the catalog grows
- Skill\-authoring labs where participants build against their own workflows, hands\-on
- Executive coaching sessions, one\-on\-one and small\-group, with leadership teams
- Demo factory facilitation: working with customers to build 3\-5 lighthouse use cases per engagement
- Train\-the\-trainer certification for customer\-side internal champions
- Community\-of\-practice setup and the first months of skill\-share cadence
2\. Connective tissue:
- The "study your work" exercise that surfaces customer use case candidates
- Post\-session briefs to the Provectus account team naming offerings the customer signaled interest in
- Maintenance of the demo library, curriculum decks, and skill catalog used in training
- Contributions back to the Provectus enablement playbook as the curriculum matures
What this role hands off:
- Custom connector engineering. Handed to Provectus integration engineers.
- ROI scorecard, value\-realization governance, and program measurement. Handed to the Provectus advisory practice.
- IT provisioning, RBAC, MDM, and marketplace governance. Handed to the Provectus platform engineers.
- Skill codification sprints that go beyond the lab format. Handed to the Provectus knowledge engineering practice.
- Commercial conversations and contracting. Handed to the account team.
### What we look for:
- The core profile is teacher plus evangelist plus consultant. Background can come from any of those worlds.
- Teaching passion. You light up when someone gets unstuck. You have done this for a living, formally or informally: workshop facilitator, bootcamp instructor, university lecturer, internal enablement lead, technical trainer.
- Public speaking. Comfortable on stage, on camera, and in a boardroom. You have run sessions for groups of fifty and one\-on\-ones with a CFO in the same week.
- Developer relations or evangelism. You have carried a technical product's message into a community that did not start as believers. You know how to make a room want something it did not know it needed.
- Consulting posture. You listen first. Diagnose second. Recommend third. You know the difference between giving an answer and earning one.
- Emotional intelligence. You read the quiet room. You hear the question behind the question. You hold steady when a senior person is dismissive.
### What we will teach you:
Deep Cowork, Claude, or agentic\-AI expertise is not required on day one.
We will teach you:
- The Cowork platform and the four building blocks (Connectors, Skills, Plugins, Automation).
- Skill authoring and the \`/skill\-creator\` workflow.
- MCP and how connectors hook into customer systems.
- The Anthropic baseline curriculum and the Provectus extensions.
- The Provectus discovery and qualification playbook.
What you need to bring: working knowledge of business software (CRM, ERP, BI, Office, productivity suites) and hands\-on familiarity with at least one general\-purpose AI tool (Claude, ChatGPT, Copilot, Gemini, or similar). Writing code is not required.
### Required posture:
- Executive presence. Holds equal weight in a room with a CFO, a CIO, and a head of operations.
- Curiosity over evangelism. The customer's workflow is the unit of analysis. The Cowork feature list is not.
- Honest unknown. Does not bluff. Follows up.
- Top\-down conviction. Believes Cowork is a platform for redesigning how a business operates, not a productivity tool for individuals. Can defend that position when the room pushes back.
Disqualifiers:
- Slide readers. If the deck is the session, this is not the role.
- Lecturers. We teach by working alongside the customer, not by talking at them.
- Low room\-read. People who steamroll quiet participants or miss the senior person's discomfort.
- Bluffers. Anyone who would rather guess than say "I do not know."
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. 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.
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 Provectus, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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.
Provectus AI Hiring
Provectus has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 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.
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