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Mlops Contract Jobs (NOW HIRING)

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 ...

Staffingine LLC

AIML Engineer

Scottsdale, AZ · On-site

Contract to Hire * Design and implement scalable MLOps supportive data pipelines for data ingestion processing and storage * Experience deploying models with MLOps tools such as Vertex Pipelines ...

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How much do mlops contract jobs pay per hour?

As of Sep 5, 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.

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 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.

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 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.

Is MLOps in high demand?

MLOps roles are in high demand due to the increasing adoption of machine learning and AI across industries. Professionals with skills in cloud platforms, automation, and tools like Docker, Kubernetes, and CI/CD pipelines are particularly sought after. The field offers strong job growth prospects and competitive salaries for qualified candidates.
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Mlops Contract jobs near you
Infographic showing various Mlops Contract job openings in the United States as of August 2026, with employment types broken down into 95% Full Time, 1% Part Time, and 4% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $54,445 per year, or $26.2 per hour.

SRE with MLops Platform

Sage IT Inc

Sunnyvale, CA • On-site

$67 - $89/hr

Contractor

Re-posted 25 days ago

Job description

Role: Site Reliability Engineer SRE – ML platform

Location: Austin, TX and Sunnyvale, CA (Onsite)

Job Type: Long Term Contract

Job Summary –

For this role, we are looking for ML Ops Engineer with Kubernetes and Python.

Experience Required:

  • 6 Plus years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.

Technical skills:

  • Python, Kubernetes, Mongo DB, Microservices, AWS
  • SOLR
  • ML operations, CI CD pipelines, LLM
  • Good understanding of Apache SOLR
  • Proficient with Linux administration.
  • Knowledge of ML models and LLM.
  • Ability to understand tools used by data scientists and experience with software development and test automation
  • Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure or GCP).

Qualifications:

  • Experience working with cloud computing and database systems
  • Experience building custom integrations between cloud-based systems using APIs
  • Experience developing and maintaining ML systems built with open-source tools
  • Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes
  • Experience developing containers and Kubernetes in cloud computing environments
  • Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
  • Ability to translate business needs to technical requirements
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Exposure to machine learning methodology and best practices
  • Good communication skills and ability to work in a team