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Open nowPosted 44 hours ago

MLOps Engineer (C2H)

edgeqtechnologies31 open roles

Pay
$110,000 – $250,000 a yearly
Where
Denver City, TX
Work mode
On site
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Your applicationOpen nowMLOps Engineer (C2H)edgeqtechnologies · Denver City, TX

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  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 44 hours ago

The posting

MLOps Engineer

Job Title: MLOps Engineer Job Type: Contract-to-Hire (C2H) Experience: Freshers to 10 Years Annual Salary: $110,000–$250,000, based on experience and skills Work Mode: On-site / Hybrid / Remote, depending on client requirements Location: United States only Employment Type: Contract with the opportunity for permanent employment

Job Summary

We are seeking motivated MLOps Engineers to build, automate, deploy, and maintain scalable machine learning infrastructure and production AI systems. Freshers with relevant projects and experienced professionals are welcome to apply.

Candidates should have knowledge of Python, machine learning pipelines, Kubernetes, MLflow, CI/CD, and model deployment practices. Candidates must currently reside in the United States and be legally authorized to work under the proposed employment arrangement.

Key Responsibilities

  • Build and maintain automated machine learning pipelines.
  • Deploy and manage ML models in production environments.
  • Use Kubernetes and Docker to containerize and orchestrate ML workloads.
  • Implement CI/CD pipelines for model training, testing, deployment, and monitoring.
  • Use MLflow for experiment tracking, model versioning, and model lifecycle management.
  • Develop automation scripts and infrastructure tools using Python.
  • Monitor model performance, reliability, latency, and resource utilization.
  • Implement model monitoring, data validation, and model retraining workflows.
  • Collaborate with data scientists, ML engineers, DevOps engineers, and software developers.
  • Maintain documentation, version control, security, and deployment standards.

Required Technical Skills

  • Programming: Python, Bash or shell scripting
  • MLOps: ML pipelines, model deployment, model versioning
  • Experiment Tracking: MLflow
  • Containerization: Docker
  • Orchestration: Kubernetes
  • CI/CD: GitHub Actions, Jenkins, GitLab CI/CD, or similar tools
  • Cloud Platforms: AWS, Microsoft Azure, or Google Cloud
  • Version Control: Git, GitHub
  • Monitoring: Prometheus, Grafana, or comparable tools
  • Machine Learning Fundamentals: Model training, evaluation, inference, and monitoring

Experience Requirements

Freshers (0–1 year)

  • Basic knowledge of Python, machine learning, and software development.
  • Understanding of Docker, Git, CI/CD, or cloud fundamentals is beneficial.
  • Academic projects, internships, certifications, or personal MLOps projects are acceptable.

Junior Engineers (1–3 years)

  • Experience with Python automation and ML workflow development.
  • Familiarity with model deployment, experiment tracking, and containerization.

Mid-Level Engineers (3–5 years)

  • Hands-on experience building ML pipelines and deploying models.
  • Knowledge of Kubernetes, MLflow, CI/CD automation, and cloud services.

Senior Engineers (5–7 years)

  • Experience managing production ML systems and scalable infrastructure.
  • Strong knowledge of monitoring, model lifecycle management, and deployment automation.

Lead / Senior-Level Engineers (7–10 years)

  • Expertise in designing end-to-end MLOps platforms and production ML architecture.
  • Experience optimizing large-scale ML workloads, reliability, security, and deployment processes.
  • Ability to establish best practices and mentor engineering teams.

Experience-Based Salary Range

ExperienceEstimated Annual SalaryFresher – 1 year$110,000–$125,0001–3 years$120,000–$150,0003–5 years$145,000–$180,0005–7 years$175,000–$215,0007–10 years$210,000–$250,000

These are indicative salary targets, not guaranteed offers. Actual compensation depends on location, skills, client budget, experience, and employment terms. Entry-level compensation may be lower than the overall advertised range.

10 U.S. Hiring Locations

  1. San Francisco, California
  2. New York City, New York
  3. Seattle, Washington
  4. Austin, Texas
  5. Boston, Massachusetts
  6. Dallas, Texas
  7. Chicago, Illinois
  8. Denver, Colorado
  9. Atlanta, Georgia
  10. Los Angeles, California

Employment Details

  • Contract Type: Contract-to-Hire (C2H)
  • Contract Duration: Determined by the hiring client.
  • Permanent Conversion: Possible based on performance, business needs, and mutual agreement.
  • Work Arrangement: On-site, hybrid, or remote, depending on the role.
  • Candidate Location: Applicants must currently reside in the United States.
  • Work Authorization: Candidates must be legally authorized to work in the U.S. under the proposed arrangement. Employer sponsorship availability varies.

Preferred Qualifications

  • Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field is preferred.
  • Experience with Terraform, Helm, Airflow, Kubeflow, or feature stores is an advantage.
  • Familiarity with Infrastructure as Code, cloud-native architecture, and automated testing.
  • A portfolio of machine learning deployment or MLOps projects is beneficial, particularly for freshers.

How to Apply

Submit an updated resume highlighting your Python skills, ML pipelines, Kubernetes, MLflow, CI/CD experience, and relevant projects.

Please include your current U.S. location, work authorization status, availability, and preferred work arrangement.

Freshers with relevant projects and strong technical fundamentals are encouraged to apply.

Equal Opportunity Employer: All qualified applicants will be considered in accordance with applicable employment laws.

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