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About This Role
Minnesota \- Developer
#### Hollstadt Overview
Hollstadt Consulting is a management and technology consulting firm dedicated to placing professionals at engagements where they will excel. When you work with us, you'll work with a refreshingly real company led and staffed by seasoned experts who are also down\-to\-earth, good people. We're committed to treating you with respect and helping you achieve your career aspirations.
Since 1990, Hollstadt has been a trusted partner to more than 150 domestic and global companies and has successfully completed over 3,000 projects. Our continued growth has created challenging and rewarding opportunities for accomplished IT and Business Consultants. Hollstadt Consulting is an equal opportunity employer including disability/veteran.
*By applying for this job, you agree to receive calls, AI\-generated calls, text messages, or emails from Hollstadt Consulting and its affiliates, and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel at any time.*
Job Description
Role: Senior Consultant, AI/ML Ops Engineer
Location: Remote
Duration: 9/1/2026\-2/1/2027
Rate: $79\-$88/hour W2
Description of work / project:
We're operationalizing machine learning across NMDP — from survival models that inform donor recommendations to LLM pipelines processing clinical documents and call transcripts. We're looking for a Senior MLOps Engineer who lives at the intersection of data science, DevOps, and platform engineering: someone who can take a model from a data scientist's notebook to a monitored, versioned, cross\-account production endpoint with full CI/CD — and own the platform that lets the whole team do the same.
This is a hands\-on senior role. You'll build and maintain the SageMaker\-based training and inference platform, the Terraform that provisions it, and the GitLab pipelines that ship it. You'll also be a force multiplier: setting the standards, patterns, and guardrails other engineers and data scientists build on.
Performance Expectations:
- Own the ML lifecycle end\-to\-end — build and operate SageMaker training pipelines and inference endpoints, model registry, versioning, promotion (validation live), and blue/green deployment with automated rollback.
- Build MLOps infrastructure as code — author and review Terraform for SageMaker, S3, KMS, IAM, CloudWatch, and cross\-account roles across multiple AWS accounts (dev/eng/prod).
- Run the CI/CD — design GitLab CI/CD pipelines covering testing, IaC security scanning (Checkov), SAST (SonarQube), dependency checks, Terraform plan/apply gates, and automated model/artifact promotion.
- Ensure train/serve parity — maintain shared feature\-encoding pipelines so preprocessing is identical in training and inference; catch drift before it ships.
- Instrument for production — CloudWatch dashboards, alarms, model/data drift monitoring, and endpoint\-level observability. Own the on\-call story for ML services.
- Harden and govern — least\-privilege cross\-account IAM, KMS encryption, model artifact signing, secrets management, and cost visibility for GPU/inference and Bedrock spend.
- Operationalize LLM workloads — support Bedrock\-based pipelines (batch and real\-time), including throughput/cost management and guardrails.
- Partner with data scientists — turn experimental models (XGBoost, survival models, etc.) into reproducible, tested, deployable services, and coach the team on MLOps best practices.
- Bring AIOps to the platform — add anomaly detection on endpoint, drift, and cost signals, and event\-driven auto\-remediation (auto\-rollback, scaling) so operational issues are caught and handled before they escalate.
- Reduce operational toil with AI — use LLM\-assisted log/trace analysis and alert correlation to speed up incident triage and root\-cause analysis, cutting MTTR and on\-call noise.
Required Qualifications
- 5\+ years in MLOps / ML platform / ML infrastructure engineering, with production ownership of deployed models.
- Deep AWS expertise, especially SageMaker (training jobs, pipelines, model registry, endpoints), plus S3, IAM, KMS, CloudWatch, Lambda, Step Functions, and multi\-account architectures.
- Strong DevOps foundation: Infrastructure as Code with Terraform, CI/CD pipeline design (GitLab CI, GitHub Actions, or similar), Docker, and Git\-based workflows.
- Strong data science fundamentals — you understand model training, evaluation metrics (e.g., C\-index, AUC, calibration), feature engineering, and can reason about why a model behaves as it does in production.
- Proficient in Python (production\-grade, tested code) for both ML tooling and services.
- Experience with model monitoring, drift detection, and the operational realities of models degrading over time.
- Solid grasp of security best practices: least\-privilege IAM, secrets management, encryption at rest/in transit.
### Benefits \+ Perks
Comprehensive Benefit Plan
Hollstadt offers medical, dental, vision, life insurance, short\-term disability, long\-term disability, paid sick leave, and retirement benefits to eligible employees. With three different medical plans to choose from, you can enroll in the coverage you need from individual to family, or anywhere in between!
Remarketing Process
Hollstadt is based on retention and relationships. We get to know your strengths and career wishes throughout your assignment and then start remarket discussions 6\-8 weeks prior to your end date. By being proactive, we are able to keep your down time between assignments as short as possible, unless you choose otherwise.
Professional Development
Hollstadt offers on\-demand training through our consultant portal. Trainings give our consultants the continuing education they need to excel on their projects. Many of our courses apply towards continuing education credits and we have an entire training hub dedicated to upskilling in Artificial Intelligence (AI).
401k \+ Matching
One popular benefit is our 401(k) match on the first 4% of your contributions. Hollstadt wants to help you reach your long\-term financial goals and understands that planning for your future is critical. Consultants also have access to support from a Financial Advisor.
Bonus Opportunities
We appreciate and reward loyalty. Join Hollstadt, stay for 5 years, and we’ll give you a $5,000 Longevity Award bonus! Additionally, we know great talent knows other great talent. If you are on contract with Hollstadt and refer one of your connections who gets placed, we’ll pay you $1,000!
Ongoing Support \& Networking
We have made a significant investment in building a support program for our consultant team \- so you never have to feel like you are going it alone. We also have a Consultant Coach program which acts like a 'work buddy' to provide a safe ear for questions or concerns at your client site.
Salary Context
This $164K-$183K range is above the median for MLOps Engineer roles in our dataset (median: $168K across 34 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 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Hollstadt Consulting, 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 $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($173K) sits 14% below the category median. Disclosed range: $164K to $183K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Hollstadt Consulting AI Hiring
Hollstadt Consulting has 5 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in MN, US. Compensation range: $140K - $185K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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