MLOps Engineer

• Job ID:

J53607

• Job Title:

MLOps Engineer

• Location:

Chicago, IL

• Duration:

18 Months + Extension

• Domain Exposure:

Utilities/ Energy

• Work Authorization:

US Citizen,Green Card,OPT-EAD,CPT,H-1B,H4-EAD,L2-EAD,GC-EAD

• Client:

To Be Discussed Later

• Employment Type:

W-2, 1099, C2C

Job Description

KeyResponsibilities:

• Design, develop, and maintain end-to-end MLOps pipelines for model development, training, deployment, and monitoring.
• Build and manage CI/CD/CT pipelines for automated model testing, validation, deployment, and retraining.
• Deploy and manage machine learning models in production environments across cloud platforms.
• Implement model versioning, experiment tracking, model registries, and artifact management.
• Build and maintain containerized ML applications using Docker and Kubernetes.
• Automate infrastructure provisioning and configuration management using Terraform, Ansible, or similar tools.
• Implement model monitoring, performance tracking, data drift detection, and model drift detection.
• Collaborate with Data Scientists and ML Engineers to optimize model deployment workflows.
• Manage cloud infrastructure and services across AWS, Azure, or GCP.
• Ensure system reliability, scalability, security, and cost optimization for ML workloads.
• Troubleshoot production issues and continuously improve ML deployment and operational processes.

Required Technical Skills:

1. MLOps & Machine Learning

• Hands-on experience with MLOps frameworks and tools.
• Experience managing the complete ML lifecycle.
• ML model deployment, monitoring, and versioning.
• Familiarity with MLflow, Kubeflow, or Metaflow.
• Understanding of Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

2. DevOps & CI/CD

• Strong experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
• Hands-on experience with Docker and Kubernetes.
• Infrastructure as Code (IaC) using Terraform, CloudFormation, or Ansible.
• Experience with Git, automation scripting, and deployment pipelines.

3. Cloud Platforms

• Strong experience with at least one major cloud platform:
• + AWS (SageMaker, EKS, S3, EC2, Lambda)

+ Microsoft Azure (Azure ML, AKS, Azure DevOps)

+ Google Cloud Platform (Vertex AI, GKE, Cloud Storage)

4. Monitoring & Observability

• Experience with Prometheus, Grafana, ELK Stack, or similar tools.
• Monitoring model performance, latency, availability, and resource utilization.
• Understanding of logging, alerting, and production troubleshooting.

5. Programming & Scripting

• Strong Python scripting skills.
• Familiarity with Bash or Shell scripting.
• Working knowledge of APIs, REST services, and microservices architecture.

Preferred Qualifications:

• Experience building scalable ML infrastructure and distributed training environments.
• Familiarity with feature stores and data orchestration tools such as Airflow.
• Experience with GPU-based workloads and ML model optimization.
• Understanding of LLMOps, GenAI deployment, and serving frameworks.
• Knowledge of security, governance, and compliance practices for ML systems.

Equal Opportunity

Employer

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs.

We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.