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About This Role
Software Engineer, MLOps
About Us
Health Data Analytics Institute (HDAI) is a commercial\-stage HealthTech company. Our Intelligent Health Management System, HealthVision™, is a first\-in\-class enterprise solution leveraging real\-time predictive analytics and generative AI to deliver improved outcomes, efficiencies, and economics for health systems and value\-based care organizations.
HDAI is located in Dedham, MA near the Dedham Corporate Center Commuter Rail station on the Franklin/Foxboro line (25 minutes from Back Bay and South Station, Boston) and I\-95/128\.
Summary
We are looking for a Software Engineer to join the ML Operations team at HDAI. You will own the services that bring HealthVision™’s predictive intelligence to life, taking data science risk models and delivering real\-time risk scores for clinical conditions and events. You will also build and maintain LLM\-powered services that surface actionable insights to clinicians and care teams.
This role requires someone who is energized rather than frustrated by ambiguity. At HDAI, requirements evolve as we scale to new health system partners and deepen our AI capabilities, and the best engineers here are the ones who can orient quickly, make sound decisions with incomplete information, and move the work forward without waiting for a perfect specification. You will work closely with data scientists, cloud engineers, and clinical stakeholders — translating between those worlds is a core part of the job. At HDAI, we nurture a culture of continuous improvement, and one of the best assets of our engineers is a love for learning. A successful candidate will demonstrate strong ownership, clear communication, and sound technical judgment.
Responsibilities
- Own the productionalization of predictive risk models — this is the core of the role. Take data science artifacts from development into reliable, monitored production services that serve real\-time risk scores, and become the internal expert on how our model delivery pipeline works end to end.
- Build and maintain LLM\-powered services on AWS Lambda and Amazon Bedrock that summarize clinical data and generate inferences to support care decisions.
- Partner with data science to plan new model releases and new data science features for the platform.
- Champion standards for code quality and application performance; maintain software using agile best practices including bug fixes, release notes, documentation, and backlog grooming.
- Contribute to a culture of continuous learning, growth, and innovation within the ML Operations team and across the broader engineering organization.
Minimum Qualifications
- 4\+ years of working experience in a data science role, as a machine learning engineer, or as an MLOps engineer with a background in mathematics, statistics, computer science, or an equivalent quantitative field of study.
- Demonstrated experience taking machine learning models from development into production — you understand the full lifecycle from model artifact handoff through inference, monitoring, and maintenance.
- Hands\-on experience building and operating LLM\-integrated services, including prompt design, output validation, and managing the failure modes of generative AI in production.
- Extensive coding experience in Python, R, or similar data/statistical programming language.
- Familiarity with managing cloud platforms (AWS, Azure, GCP) and Docker; experience with Terraform is a plus.
- Proficient in implementing event\-driven architectures composed of stateless services.
- Comfortable working with Git and on a team using agile/scrum.
- Demonstrated ability to operate effectively in ambiguous, fast\-moving environments — you can set direction and make progress when requirements are incomplete or evolving.
- Experience in healthcare or an adjacent regulated industry is a plus.
- Experience in a startup environment is preferred
- Excellent interpersonal skills with demonstrated ability to effectively communicate with internal and external teams; ability to develop trust, cooperation, and mutual respect.
Pay: $125,453\.00 \- $160,567\.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Flexible schedule
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Referral program
- Retirement plan
- Tuition reimbursement
- Vision insurance
Work Location: Hybrid remote in Dedham, MA 02026
Salary Context
This $125K-$160K range is in the lower quartile for MLOps Engineer roles in our dataset (median: $209K across 26 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Health Data Analytics Institute, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 76 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($143K) sits 34% below the category median. Disclosed range: $125K to $160K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Health Data Analytics Institute AI Hiring
Health Data Analytics Institute has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Based in Dedham, MA, US. Compensation range: $160K - $230K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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