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About This Role
Role description ML Platform Engineer
ML Engineer I
Who We Are:
Born digital, UST transforms lives through the power of technology. We walk alongside our clients and partners, embedding innovation and agility into everything they do. We help them create transformative experiences and human\-centered solutions for a better world.
UST is a mission\-driven group of 29,000\+ practical problem solvers and creative thinkers in more than 30 countries. Our entrepreneurial teams are empowered to innovate, act nimbly, and create a lasting and sustainable impact for our clients, their customers, and the communities in which we live.
With us, you’ll create a boundless impact that transforms your career—and the lives of people across the world.
Visit us at UST.com.
You Are:
UST is searching for an ML Platform Engineer who will build MCP Servers for CIE (expose scoring, rules, explainability as MCP tools via Model Context Protocol).
The opportunity:
- Integrate with AgentCore Gateway (JWT/mTLS authentication, entitlement checks, routing)
- Deploy BHI custom models to Databricks Model Serving \+ Snowflake (UDFs, external functions)
- AWS Bedrock integration for LLM explainability via AI Marketplace subscription
- PHI guardrails implementation (pre\-request scanning, post\-response masking for healthcare data)
- MLflow model registry (versioning, A/B testing, rollback)
- Build CI/CD for model deployment, Feature store integration (Databricks Feature Store MCP tools)
- OpenTelemetry instrumentation (usage events emission to AI Marketplace metering)
- MCP tool manifest registration (score\_claim, explain\_detection, provider\_profile tools)
This position description identifies the responsibilities and tasks typically associated with the performance of the position. Other relevant essential functions may be required.
What you need:
- AWS/ Databricks Certified (preferred) Python 5\+ years
- Familiarity with MCP Protocol (Model Context Protocol) \- build MCP servers that expose tools, MLOps/MLflow 3\+ years (model registry, serving) on AWS and Databricks
- Databricks Model Serving production (REST API endpoints)
- Snowflake UDF \+ external function deployment
- OAuth2/JWT authentication \+ mTLS certificate handling, AWS Bedrock (LLM integration for explainability)
- AgentCore Gateway integration (JWT validation, entitlement enforcement, tool routing), PHI/HIPAA compliance (pre/post\-request PHI scanning), OpenTelemetry (usage event emission, distributed tracing), REST API development (MCP tool endpoints), Docker/containerization
- Monitoring (Databricks, CloudWatch), Git \+ GitHub \+ CI/CD
- Must have 3\-5 years MLOps with AWS \+ Databricks \+ Snowflake
- Must understand MCP Protocol or willingness to learn (AWS AgentCore documentation)
- Must have worked with OAuth2\-secured APIs
Compensation can differ depending on factors including but not limited to the specific office location, role, skill set, education, and level of experience. UST provides a reasonable range of compensation for roles that may be hired in various U.S. markets as set forth below.
Role Location: Remote\-US
Compensation Range: $72,000\-$108,000
Benefits
Full\-time, regular employees accrue a minimum of 10 days of paid vacation per year, receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year), 10 paid holidays, and are eligible for paid bereavement leave and jury duty. They are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance, as well as the following Company\-paid Employee Only benefits: basic life insurance, accidental death and disability insurance, and short\- and long\-term disability benefits. Regular employees may purchase additional voluntary short\-term disability benefits, and participate in a Health Savings Account (HSA) as well as a Flexible Spending Account (FSA) for healthcare, dependent child care, and/or commuting expenses as allowable under IRS guidelines. Benefits offerings vary in Puerto Rico.
Part\-time employees receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year) and are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching.
Full\-time temporary employees receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year) and are eligible to participate in the Company’s 401(k) program with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance.
Part\-time temporary employees receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year).
All US employees who work in a state or locality with more generous paid sick leave benefits than specified here will receive the benefit of those sick leave laws.
What we believe:
We proudly embrace the values that have shaped UST since day one. We build our culture of Humility, Humanity, and Integrity. These values inspire us to nurture a people\-first, human centric culture that fosters diversity, prioritizes sustainable solutions, and keeps our people and clients at the forefront of all decisions.
Humility:
We will listen, learn, be empathetic and help selflessly in our interactions with everyone.
Humanity:
Through business, we will better the lives of those less fortunate than ourselves.
Integrity:
We honor our commitments and act with responsibility in all our relationships.
Equal Employment Opportunity Statement
UST is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other applicable characteristics protected by law. We will consider qualified applicants with arrest or conviction records in accordance with state and local laws and “fair chance” ordinances.
UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance.
\#UST
\#CB
\#LI\-AP6
\#LI\-Remote
Skills
aws,hipaa compliance,aws databricks,json web token,rest api development,cicd,docker containerization,
Benefits
Compensation range: $72,000\.00 to $108,000\.00 per year
About UST
UST is a global digital transformation solutions provider. For more than 20 years, UST has worked side by side with the world’s best companies to make a real impact through transformation. Powered by technology, inspired by people and led by purpose, UST partners with their clients from design to operation. With deep domain expertise and a future\-proof philosophy, UST embeds innovation and agility into their clients’ organizations. With over 30,000 employees in 30 countries, UST builds for boundless impact—touching billions of lives in the process.
Salary Context
This $72K-$108K range is in the lower quartile for MLOps Engineer roles in our dataset (median: $177K across 20 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 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At UST, 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 $220,000 based on 47 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($90K) sits 59% below the category median. Disclosed range: $72K to $108K.
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.
UST AI Hiring
UST has 3 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Santa Clara, CA, US, Chicago, IL, US. Compensation range: $108K - $159K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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 3,708 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).
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 3,708 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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