About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, IT.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

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Job Summary

Confidential Client is hiring an MLOps Engineer (Contract-to-Hire) in Denver City, TX. This role focuses on building, automating, deploying, and maintaining scalable machine-learning infrastructure and production AI systems. Candidates across experience levels (freshers through senior) with hands-on MLOps experience or relevant projects are encouraged to apply. Applicants must reside in the United States and be authorized to work here under the proposed arrangement.

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Key Responsibilities

• Build, automate, and maintain reproducible ML pipelines for training, evaluation, and inference.
• Deploy, scale, and manage ML models in production environments.
• Containerize workloads with Docker and orchestrate using Kubernetes.
• Design and implement CI/CD pipelines for model training, testing, deployment, and rollback.
• Track experiments, manage model versions, and support model lifecycle using MLflow (or similar).
• Create Python automation scripts and infrastructure tools for repeatable workflows.
• Monitor model performance, latency, reliability, and resource utilization; implement alerting.
• Implement data validation, model monitoring, and automated retraining workflows.
• Collaborate with data scientists, ML engineers, DevOps, and software teams to operationalize models.
• Maintain documentation, version control, security practices, and deployment standards.

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Required Technical Skills

• Strong programming in Python; Bash/shell scripting experience.
• Experience building ML pipelines and model deployment workflows.
• Hands-on with MLflow (experiment tracking & model versioning).
• Containerization with Docker.
• Orchestration with Kubernetes.
• CI/CD experience (GitHub Actions, Jenkins, GitLab CI/CD, or similar).
• Familiarity with at least one cloud platform: AWS, Azure, or GCP.
• Version control with Git / GitHub.
• Monitoring tools such as Prometheus, Grafana, or equivalents.
• Solid understanding of model training, evaluation, inference, and monitoring best practices.

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Experience Levels

• Freshers (0–1 year): Academic projects, internships, or personal MLOps projects; foundational Python and ML knowledge.
• Junior (1–3 years): Python automation, basic pipeline work, exposure to containerization and experiment tracking.
• Mid-level (3–5 years): Hands-on pipeline development, model deployment, Kubernetes usage, and cloud integrations.
• Senior (5–7 years): Managing production ML systems, lifecycle management, monitoring, and automation at scale.
• Lead (7–10 years): Design end-to-end MLOps platforms, optimize large-scale workloads, set best practices, and mentor teams.

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Compensation

• Indicative annual range: $110,000 – $250,000 (experience-based).
  • Fresher – 1 year: $110,000–$125,000
  • 1–3 years: $120,000–$150,000
  • 3–5 years: $145,000–$180,000
  • 5–7 years: $175,000–$215,000
  • 7–10 years: $210,000–$250,000

(These are target ranges. Actual offers depend on location, skills, client budget, and employment terms.)

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Employment Details

• Contract Type: Contract-to-Hire (C2H) — contract with potential for permanent conversion.
• Work Arrangement: On-site, hybrid, or remote depending on the role and client requirements.
• Candidate Location: Applicants must currently reside in the United States.
• Work Authorization: Must be legally authorized to work in the U.S.; employer sponsorship availability varies.

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Preferred Qualifications

• Bachelor’s or Master’s in Computer Science, Data Science, AI, or related field preferred.
• Experience with Terraform, Helm, Airflow, Kubeflow, or feature-store tooling is a plus.
• Familiarity with Infrastructure-as-Code, cloud-native architecture, and automated testing.
• A portfolio of MLOps projects or deployed models is beneficial — especially for entry-level candidates.

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What We Value

• Practical, production-oriented MLOps experience and a strong automation mindset.
• Clear communication and cross-functional collaboration skills.
• Security-first thinking when deploying production ML systems.
• A continuous improvement approach to observability, reliability, and cost-efficiency.

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Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

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