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About This Role
Overview:
We are seeking a MLOps Engineer to design, build, and support the infrastructure, tooling, and automation that enable scalable and reliable machine learning systems across our client engagements. This role is responsible for operationalizing ML models, implementing robust pipelines, and ensuring smooth transitions from experimentation to production. The MLOps Engineer works closely with Data Scientists, AI Developers, Data Engineers, and cloud engineering teams to streamline model deployment, monitoring, and lifecycle management in alignment with mission needs.
Contributions:
- Develop and maintain end\-to\-end ML pipelines, including data ingestion, feature engineering, model training, model packaging, deployment, and monitoring workflows.
- Implement CI/CD pipelines for ML assets, enabling automated testing, versioning, promotion, and reproducibility across environments.
- Integrate ML models into production services using APIs, microservices, serverless functions, or container orchestration frameworks like Kubernetes.
- Build and manage core ML platform components such as model registries, experiment tracking systems, feature stores, datasets, job schedulers, and lineage tools.
- Monitor model performance, system health, and data drift using logging, observability frameworks, dashboards, and alerting systems; partner with Data Scientists to refine retraining strategies.
- Collaborate with Data Engineers to ensure data pipelines and data quality support high\-performing ML systems.
- Implement DevSecOps best practices—including secrets management, environment hardening, and secure deployment patterns—to ensure compliance and operational resilience.
- Help define and enforce MLOps standards, documentation, and reusable patterns that improve efficiency and reduce technical debt across teams.
- Support troubleshooting and root\-cause analysis of pipeline issues, infrastructure problems, or performance degradation in deployed ML models.
- Stay current with emerging MLOps tools, cloud\-native ML technologies, distributed training methodologies, and best practices in ML lifecycle management.
- You will contribute to the growth of our AI \& Data Exploitation Practice!
Qualifications:
- Ability to hold a position of public trust with the U.S. government.
- Bachelors or Master’s degree in Computer Science, Data Engineering, Machine Learning, Information Systems, or a related technical discipline.
- Masters Degree and 0 years of experience OR Bachelors Degree and 2 years of experience OR No degree and 6 years of experience.
- 2\+ years of experience in MLOps, ML engineering, DevOps, cloud engineering, or applied ML development.
- Proficiency in Python and familiarity with ML frameworks such as scikit\-learn, TensorFlow, PyTorch, or XGBoost.
- Hands\-on experience with at least one cloud platform (AWS, Azure, or GCP) and associated ML/DevOps services (e.g., SageMaker, Azure ML, Vertex AI, EKS/AKS/GKE).
- Practical experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins) and containerization (Docker, Kubernetes).
- Strong understanding of ML lifecycle management, including versioning, packaging, deployment, monitoring, and retraining.
- Familiarity with infrastructure\-as\-code tools such as Terraform or CloudFormation.
- Experience with logging, observability, and monitoring frameworks (CloudWatch, Prometheus, Grafana, ELK stack, Datadog, etc.).
- Ability to collaborate with Data Scientists, Engineers, and mission stakeholders to ensure ML systems deliver operational value.
- Strong communication skills and the ability to document workflows, architecture decisions, and runbooks.
- Preferred certifications:
+ AWS ML Specialty
+ AWS DevOps Engineer
+ Azure Data Scientist Associate
+ Google Professional Machine Learning Engineer
+ Databricks Machine Learning Associate/Professional
About steampunk:
Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $150,000\. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk’s total compensation package for employees. Learn more about additional Steampunk benefits here.
Identity Statement
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human\-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. As an employee owned company, we focus on investing in our employees to enable them to do the greatest work of their careers – and rewarding them for outstanding contributions to our growth. If you want to learn more about our story, visit http://www.steampunk.com.
Salary Context
This $115K-$150K range is below 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 Steampunk, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($132K) sits 35% below the category median. Disclosed range: $115K to $150K.
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.
Steampunk AI Hiring
Steampunk has 3 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer, Data Scientist. Based in McLean, VA, US. Compensation range: $145K - $160K.
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.
Frequently Asked Questions
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