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About This Role
Provectus is a Premier AWS Partner and an Anthropic Strategic Partner, featured in Forrester's AI Technical Services Landscape. We are roughly 420 engineers building production AI for enterprise clients in Financial Services, Insurance, Healthcare and Life Sciences, Retail, and Entertainment. We build custom generative AI and agentic AI systems on live client data, not off the shelf products and not demos.
We are hiring a Senior Solution/Product Owner for our AI Delivery practice, a forward deployed seat at the intersection of frontier AI and enterprise transformation. You will own both the story and the delivery for client engagements built on Cowork Activation, Agentic SDLC with Claude Code, and Blueprint Activation. In one week you might frame an ROI case for a CFO, align a healthcare or financial services stakeholder on a compliant GenAI rollout, and pair with a Forward Deployed Engineer to wire an MCP integration or ship a Claude Code harness.
What You Will Do:
- Own client engagements end to end, from use case framing and business case through scoped delivery, first deployment, and measurable results
- Define the value proposition, positioning, and reusable artifacts for your assigned Program or Blueprint, and align sales, delivery, and account teams around them
- Build business cases covering market fit, ROI, and competitive positioning, and translate them into scoped statements of work
- Run discovery, use case validation, design thinking workshops, and user research, and turn findings into demos and production deployments
- Stand up Cowork Activation, Agentic SDLC with Claude Code, or Blueprint Activation on live client data, driving rapid prototyping from use case to context to result
- Build hands on with the Provectus delivery stack, including Claude, Claude Code, MCP servers, connectors, Skills, and plugins
- Define product vision and roadmap for GenAI, LLM, RAG, and agentic AI engagements, and judge solution quality alongside architects and engineers
- Break complex work into incremental releases, and balance delivery speed against technical debt and architecture
- Lead Agile and Scrum execution across backlog, sprint planning, and release, following our Sprint, Enable, Realize engagement model
- Define success metrics and analytics, and drive continuous improvement through root cause analysis
- Contribute to the measurement framework behind our board level AI Maturity Index, ROI signal, and cost read
- Support change management, stakeholder alignment, enablement, and reusable playbooks so wins repeat across accounts
What You Will Bring:
- 5 to 7+ years in product ownership, solution ownership, technical product management, or delivery leadership
- 3 to 5+ years working with AI/ML, generative AI, LLM applications, agentic systems, or data platforms
- Deep domain expertise in Financial Services and Insurance and/or Healthcare and Life Sciences, ideally in a client facing or consulting context
- Track record operating forward deployed or embedded with clients, as a Forward Deployed Engineer or Forward Deployed Expert, including hands on prototyping
- Proven ability to take an offering, program, or product from zero to one and land it as real client delivery
- Daily working use of Claude, Claude Code, and similar GenAI tools, including building your own skills, workflows, and productivity tooling
- Comfort with MCP (Model Context Protocol) integrations, connectors, and custom agent harnesses
- Understanding of GenAI and agentic patterns, including prompt engineering, RAG, embeddings, vector databases, and agent frameworks, deep enough to assess quality and translate it for executive stakeholders
- Data fundamentals: what ETL/ELT, data modeling, and data governance mean in an enterprise context
- Familiarity with AWS as a primary cloud platform, including Bedrock, and working awareness of MLOps and AI observability
- Strong Agile and Scrum experience, and familiarity with outcome based, milestone priced consulting engagements
- Design thinking, data driven decision making, and executive storytelling that holds up in front of a CFO or CIO
- Enough engineering empathy to earn credibility with senior engineers and solutions architects on day one
- Bias to action and structured problem solving in ambiguity
- Based in the San Francisco Bay Area or New York City metro area, and authorized to work in the US without sponsorship
Nice To Have:
- A second priority vertical beyond your primary Financial Services or Healthcare focus
- Prior experience in an AWS Premier Partner or Anthropic partner environment
- Claude Certified Architect, Seller, or Developer certification, or willingness to complete one early
- Certifications such as CSPO, SAFe PO/PM, or AWS, Azure, or GCP
- Software development or solutions engineering background
What We Offer:
- Base salary range of $140,000 to $180,000 USD depending on experience
- Frontier delivery work across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare
- The chance to shape how leading enterprises adopt AI, from strategy through first deployment
- A forward deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers
- A growing AI delivery practice where you help build the tooling and frameworks, not just use them
- Health, dental, vision, and 401(k) with company match
- Unlimited PTO
- Access to the latest AI tools and premium subscriptions
- Internal training programs with full support for AWS and Claude certifications
- Hybrid work in San Francisco or New York
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. 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.
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Provectus AI Hiring
Provectus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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