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Mlops Contract Jobs (NOW HIRING)

Niyam IT, Inc.

DevOps/MLOps Engineer

Ashburn, VA · On-site

$54 - $74/hr

Niyam is seeking a DevOps/MLOps Engineer to join our team in support of our work with a federal ... This position is contingent upon award of contract. Roles and Responsibilities * Design, implement ...

Niyam IT, Inc.

DevOps/MLOps Engineer

Ashburn, VA · On-site +1

$54 - $74/hr

Niyam is seeking a DevOps/MLOps Engineer to join our team in support of our work with a federal ... This position is contingent upon award of contract. Roles and Responsibilities * Design, implement ...

Sage IT Inc

SRE with MLops Platform

Sunnyvale, CA · On-site

$67 - $89/hr

Austin, TX and Sunnyvale, CA (Onsite) Job Type: Long Term Contract Job Summary - For this role, we ... Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud ...

CEDENT

MLOps Engineer

$40 - $60/hr

Must Have Skills: * 4+ years of MLOps/ML platform or DevOps for data/ML systems * Hands on GCP ... Define contracts for features/labels in BigQuery and manage backfills; support batch and (where ...

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How much do mlops contract jobs pay per hour?

As of Jun 11, 2026, the average hourly pay for mlops contract in the United States is $26.18, according to ZipRecruiter salary data. Most workers in this role earn between $20.19 and $28.61 per hour, depending on experience, location, and employer.

Which 3 jobs will survive AI?

For MLOps contract roles, jobs such as data scientists, machine learning engineers, and AI system architects are expected to persist as they require specialized expertise in model development, deployment, and maintenance. These roles involve complex problem-solving, domain knowledge, and skills in tools like TensorFlow or PyTorch, making them less susceptible to automation by AI. Continuous learning and certification in AI and cloud platforms can also enhance job security in this field.

What is an MLOps contract?

An MLOps contract refers to a temporary or project-based agreement for professionals who specialize in Machine Learning Operations (MLOps). MLOps combines machine learning, software engineering, and DevOps practices to streamline the deployment, monitoring, and management of machine learning models in production. These contracts typically require expertise in automation, CI/CD pipelines, cloud platforms, and model lifecycle management. Contractors are often hired to help organizations quickly implement or scale their machine learning infrastructure, ensuring models are reliable, scalable, and secure.

What are the key skills and qualifications needed to thrive as an MLOps Contract professional, and why are they important?

To thrive as an MLOps Contract professional, you need solid experience in machine learning, software engineering, and cloud infrastructure, often supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and platforms such as AWS, Azure, or GCP, along with certifications like AWS Certified Machine Learning or Google Professional ML Engineer, is highly valuable. Strong problem-solving, communication, and collaboration skills help you deliver robust solutions and work effectively with cross-functional teams. These skills ensure efficient deployment, scalability, and maintenance of machine learning models in production environments.

What engineers make $500,000?

Senior machine learning engineers, data science leads, and AI architects with extensive experience and advanced skills in deep learning, cloud platforms, and large-scale data processing can earn $500,000 or more annually. These roles often require advanced degrees, certifications, and a strong track record of delivering complex AI solutions in high-demand industries.

What is the difference between Mlops Contract vs Data Engineer?

AspectMlops ContractData Engineer
Required CredentialsCertifications in cloud platforms, scripting, and ML toolsDegree in Computer Science or related field, SQL, Python skills
Work EnvironmentProject-based, contract roles in cloud and ML teamsFull-time or contract, data pipeline development in data teams
Employer & Industry UsageTech companies, startups, consulting firmsLarge enterprises, finance, healthcare, tech
Search & Comparison IntentUnderstanding contract roles in ML operationsData pipeline and infrastructure roles

While both roles involve working with data and cloud tools, Mlops Contract focuses on deploying and maintaining machine learning models in production environments on a contractual basis. Data Engineers primarily build and manage data pipelines and infrastructure. The roles overlap in skills like scripting and cloud familiarity but differ in scope and responsibilities.

What contract job pays the most?

In the field of MLOps, contract roles such as senior machine learning engineer or lead MLOps engineer tend to offer the highest pay, often exceeding $100 per hour depending on experience and location. These positions typically require strong skills in cloud platforms, automation tools, and deployment pipelines, and may involve longer-term projects with high responsibility.

What is the average salary in MLOps?

The average salary for an MLOps engineer typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and containerization may command higher salaries. Certifications in machine learning and DevOps can also influence compensation.

What are some common challenges faced by MLOps contractors when integrating machine learning models into existing production systems?

MLOps contractors often encounter challenges such as aligning model deployment processes with an organization's existing infrastructure and ensuring seamless collaboration between data science and engineering teams. They must navigate differences in technology stacks, manage versioning of models and datasets, and address issues related to scalability and monitoring in production environments. Effective communication and a thorough understanding of both machine learning workflows and DevOps practices are key to overcoming these hurdles and delivering reliable, maintainable solutions.
More about Mlops Contract jobs
What cities are hiring for Mlops Contract jobs? Cities with the most Mlops Contract job openings:
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Mlops Contract jobs near you
Infographic showing various Mlops Contract job openings in the United States as of June 2026, with employment types broken down into 33% Full Time, and 67% Contract. Highlights an 33% In-person, and 67% Remote job distribution, with an average salary of $54,445 per year, or $26.2 per hour.

Senior Data Scientist / MLOps Engineer (Optimization Specialist)

Purple Drive Technologies

Minneapolis, MN • On-site

Full-time

Posted 29 days ago

Job description

Overview:
Mandatory Skills
Optimization Algorithms (Simulated Annealing)
MLOps & ML Pipeline Engineering (Azure preferred)
Production-Grade Python / Model Deployment
Algorithm Design & Variable Optimization
Quantum Annealing / Quantum Computing (Exposure)
Type: Contract
Industry: Advanced Analytics / Quantum Computing Research
Job Summary
We are seeking a highly skilled Data Scientist with a strong background in MLOps Engineering to lead the development and productionalization of complex optimization models. You will be responsible for not only designing the core models specifically focusing on Simulated Annealing and transitioning toward Quantum Annealing, but also building the automated pipelines required to move these models into a production environment.
Key Responsibilities
  • Core Modeling: Design and develop advanced optimization models. You will lead the "journey" from classical optimization to simulated annealing, with a future-state focus on quantum annealing.
  • MLOps & Productionization: Bridge the gap between data science and DevOps by writing production-grade code. Ensure models are scalable, reliable, and integrated into the broader ecosystem.
  • Pipeline Construction: Design and maintain robust data and ML pipelines. You will determine what data is pushed through the system and how it is processed for maximum efficiency.
  • Algorithm Selection: Lead the selection of algorithms and variables. You must understand how models "reason" and be able to justify the architectural choices for the optimization engine.
  • Deployment: Take full ownership of the model deployment lifecycle, ensuring that the "Optimization Thing" (internal use case) is fully functional in a live environment.

Technical Requirements
  • Advanced Optimization: Deep expertise in optimization algorithms, specifically Simulated Annealing. Familiarity or interest in Quantum Annealing/Quantum Computing is a significant plus.
  • Engineering Excellence: Proven ability to write production-ready code. This is not a research-only role; you must be able to "conscribe" and deploy your own work.
  • MLOps Frameworks: Strong experience in building and managing machine learning pipelines ( Azure preferred).
  • Data Science Fundamentals: Mastery of variable selection, algorithm tuning, and model evaluation metrics.

Preferred Qualifications
  • Experience with high-stakes, confidential use cases involving complex data modeling.
  • Local to Minnesota (Strongly preferred for onshore collaboration).
  • Ability to work in an agile, fast-paced environment with an "immediate" onboarding timeline.

Apply