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About This Role
Job Title: Product Operations \& AI Enablement Specialist
Location: Lakewood, CO (Hybrid)
About Bloom
Bloom Healthcare is a pioneering value\-based primary care and hospice practice at the forefront of transforming healthcare delivery for vulnerable patients. We bring high\-touch, innovative medicine to those living at home with chronic conditions. Bloom’s model of care is proven to provide exceptional care to the homebound population, and Bloom Healthcare has generated outstanding quality results in CMS Innovation Center models compared to our peers.
At Bloom Healthcare, we believe in creating an environment that fosters growth, collaboration, and a shared sense of purpose. Bloom Healthcare has been voted the "Top Workplace" for five consecutive years. This honor reflects our unwavering commitment to our employees. By nurturing a work culture that puts our team first, we empower them to put our patients first.
Position Overview
We're looking for a sharp, self\-driven Product Operations \& AI Enablement Specialist to join our small but high\-impact IT team, reporting to the Director of Emerging Technology. This is primarily a hands\-on builder role \- you'll spend most of your time designing workflow solutions, running AI pilots end\-to\-end, and finding ways to eliminate manual work across our operations.
That said, product management is a real part of this role and a clear growth path for the right person. As you build deep expertise in our products and users, strategic ownership will follow.
This is a hybrid position with 4 days per week required in our office on Lakewood, CO.
Key Responsibilities
Business Process Optimization
*This is an area of significant independent ownership. The role identifies, designs, and builds solutions to complex business process problems, working directly with business teams. We use Airtable and other no\-code/low\-code platforms, and we expect a solutions\-first mindset: staying current on new tools and bringing forward recommendations for evaluation.*
- Engage with non\-technical stakeholders to understand pain points and translate complex process problems into scalable technical solutions.
- Own the full build\-and\-launch cycle: requirements, design, build, testing, user training, and post\-launch support.
- Proactively identify opportunities to streamline processes and reduce manual work. Evaluate and recommend new tools that eliminate clunky workflows.
- Maintain and improve deployed solutions as business needs evolve. Create documentation and training guides to support team adoption.
AI Initiatives — Research \& Coordination
*This role drives hands\-on implementation and project management, directly building, deploying and operationalizing AI\-powered solutions.** Coordinate approved AI pilots: schedule working sessions, track action items, maintain project documentation, and follow up on open items.
- Coordinate approved AI pilots: schedule working sessions, track action items, maintain project documentation, and follow up on open items.
- Build and deploy no\-code AI agents and LLM\-powered automations. Design the logic, configure workflows, and iterate based on user feedback.
- Support employee adoption of LLM tools: develop training materials, prompt guides, token optimization tips, and how\-to resources tailored to specific team needs.
- Research AI tools, use cases, and industry landscape. Prepare structured briefs and recommendations for the AI Steering Committee.
- Facilitate governance: manage user licenses, track usage, outcomes, and ROI, and maintain AI policies.
Product Management \& Operational Support
*Our development team owns a mission\-critical internal application. Under the direction of the Director of Emerging Technology, this role supports execution and day\-to\-day operations.** Serve as the first point of contact for user\-reported issues. Log, triage, and investigate before escalating to developers with clear reproduction steps and context.
- Own and maintain user\-facing documentation end\-to\-end: release notes, training materials, how\-to guides, and feature walkthroughs for new and updated functionality.
- Design wireframes and UI mockups in Figma and AI\-assisted tools, turning PM direction into concrete visuals for developer handoff and stakeholder review.
- Conduct UAT for new releases: test or recruit testers, track feedback, and deliver a structured summary to the PM.
- Keep the product backlog clean and well\-organized. Tickets clearly described and consistently formatted.
- Collect user feedback through meetings, surveys and support tickets, then synthesize it into structured summaries for the development team to act on.
- Coordinate release logistics: communicate timelines to stakeholders, confirm readiness with the dev team, and prepare for trainings.
Qualifications
Required* Bachelor’s degree in Business, Information Systems, Healthcare Administration, or a related field required; Master’s degree preferred.
- 3–5 years of experience in product operations, business analysis, project coordination, or a similar execution\-focused role.
- Strong written communication skills, with the ability to create clear documentation, training materials, and summaries independently.
- Hands\-on experience with Airtable, Notion, or similar no\-code/low\-code tools.
- Highly organized, with the ability to manage multiple workstreams, deadlines, and stakeholder needs in parallel.
- Able to execute independently, collaborate effectively across teams, and contribute to a positive, solutions\-oriented team environment.
- Strong curiosity about AI, automation, and emerging technology, with a proactive approach to staying current.
- High attention to detail, strong analytical judgment, and commitment to data accuracy.
- Working knowledge of software development processes, including QA, user acceptance testing, and developer handoffs.
Nice to have* Figma or similar design tool experience
- Exposure to Agile, sprint planning, or user story writing
- Hands\-on with ChatGPT, Claude, Copilot, or similar LLM tools in practical workflows
- Healthcare experience preferred, especially in value\-based care, clinical operations, population health, or provider\-facing workflows.
- Experience with Reporting tools (Airtable dashboards, Power BI, Tableau, Looker, etc.)
What Success Looks Like* In the first 30 days, you have built strong familiarity with our internal products and tools, taken ownership of user issue triage, and contributed clear documentation and release notes. Within 90 days, you have delivered at least one no\-code or low\-code solution end\-to\-end and are actively supporting AI coordination and business process improvement projects. Within six months, you are operating with increasing independence across all three areas of the role \- internal solutions are moving forward, users are supported, and you are contributing meaningfully to product operations: requirements are well\-documented, the backlog is organized, and the development team has what they need to execute.
Why Bloom?
Bloom Healthcare is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
The salary for this position is competitive and commensurate with experience. *The pay range for this role in the state of Colorado typically falls between**$80,000 \- $115,000 annually* with the potential for performance\-based bonuses and other benefits. Actual compensation may vary based on factors such as qualifications, experience, and location within the state.
Bloom Healthcare only contacts through official channels using the @bloomhealthcare.com domain. We are aware of a fraudulent Gmail account impersonating our recruiting team and have reported it to Google.
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Salary Context
This $80K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bloom Healthcare, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($97K) sits 55% below the category median. Disclosed range: $80K to $115K.
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.
Bloom Healthcare AI Hiring
Bloom Healthcare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lakewood, CO, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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