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Mlops Contract Jobs (NOW HIRING)

IT America Inc

DevOps/MLOps Engineer

Cumming, GA · On-site

$47 - $64.50/hr

Cumming, GA Duration: Long-term contract Note: Final interview will take place onsite--only local candidates will be considered * Our Fintech client is looking for an experienced DevOps / MLOps ...

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 ...

AKIVA AI, LLC

$120 - $180/hr

... MLOps, platform engineering, or SRE for ML systems * Hands-on with at least one ML platform ... Opportunity to work on impactful client engagements * Long-term contract engagement with potential ...

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 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.

MLOPS Engineer

Tror AI for everyone

Concord, CA • On-site

Contractor

Re-posted 11 days ago

Job description

Job Role: MLOPS Engineer

Job Location:  Concord, CA (100% Onsite)

Job Type: Contract

Key Responsibilities:

  • Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI. 
  • Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
  • Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
  • Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
  • Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
  • Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment

Qualifications:

  • 10+ Years of professional experience in Software Engineering & 3+ Years in AIML, Machine Learning Model Operations.
  • Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
  • Experience with cloud platforms and containerization (Docker, Kubernetes).
  • Hands on experience delivering 3-4 end to end Production projects
  • Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
  • Solid understanding of software engineering principles and DevOps practices.
  • Good communication skills and able to manage stakeholders.
 
Thanks & Regards

Nagendra

US IT Recruiter | TROR LLC

Ph: 615-857-6282 | Email: nregella@tror.ai

LinkedIn: https://www.linkedin.com/in/nagendra-nag-1b7665b8/

Website: https://tror.ai

Address: 401 Ronan Way, Spring Hill, TN 37174