ML Ops Engineer

Job Title: ML Ops Engineer

Location: Remote

Department: Engineering

Job Type: Full-time

Who We Are
unifyCX is an emerging Global Business Process Outsourcing company with a strong presence in the U.S., Colombia, Dominican Republic, India, Jamaica, Honduras, and the Philippines. We provide personalized contact centers, business processing, and technology outsourcing solutions to clients worldwide. In nearly two decades, unifyCX has grown from a small team to a global organization with staff members all over the world dedicated to supporting our international clientele.

UnifyCX is a transformative AI platform that empowers and enables teams to deliver efficient, exceptional customer experiences. We engineer superhuman customer experiences through a powerful blend of strategy, omnichannel support, analytics, and AI-driven tools like GoTalent.AI, Voice of Customer, and automatic QA. Our outcome-based model prioritizes measurable results for more than 200 client programs serviced today. With a focus on automation, talent enablement, strategic partnerships, and strict data ethics, UnifyCX delivers scalable, personalized, and compliant solutions that create real business impact.

At unifyCX, we leverage advanced AI technologies to elevate the customer experience (CX) and drive operational efficiency for our clients. Our commitment to innovation positions us as a trusted partner, enabling businesses across industries to meet the evolving demands of a global market with agility and precision. unifyCX is a certified minority-owned business and an EOE employer who welcomes diversity.

First Key Projects
Develop the infrastructure needed for ML workloads, including cloud services, containerization, and orchestration tools.

Key Responsibilities

• Model Deployment & Monitoring: Design, implement, and manage the deployment pipelines for machine learning models. Monitor model performance and ensure the stability and scalability of production systems.
• Infrastructure Management: Develop and maintain the infrastructure needed for ML workloads, including cloud services, containerization, and orchestration tools.
• Automation: Automate the ML lifecycle processes, including data ingestion, model training, testing, and deployment.
• Collaboration: Work closely with data scientists and engineers to understand model requirements and ensure seamless integration into production systems.
• Version Control: Implement and manage version control systems for ML models, datasets, and code.
• Performance Tuning: Optimize ML models and systems for performance, cost, and efficiency. Troubleshoot and resolve issues that arise in production environments.
• Security & Compliance: Ensure that all ML systems and processes comply with security policies and regulatory requirements.
• Documentation & Reporting: Document processes, best practices, and workflows. Provide regular reports on system performance, issues, and improvements.

Required Skillsets

• Bachelor's degree in Computer Science, Data Engineering, or related field.
• 3+ years of experience in ML Ops, DevOps, or Data Engineering.
• Strong proficiency with Python and Linux-based systems.
• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Proficiency with containerization (Docker) and orchestration (Kubernetes).
• Experience with cloud platforms (AWS Sagemaker, GCP Vertex AI, or Azure ML).
• Familiarity with CI/CD pipelines and infrastructure-as-code tools (Terraform, CloudFormation).

Preferred Skillsets

• Experience with feature stores (Feast, Tecton, etc.).
• Knowledge of data pipelines and orchestration tools (Airflow, Prefect, Dagster).
• Background in MLOps frameworks (MLflow, Kubeflow, BentoML, DVC).
• Experience implementing model monitoring and drift detection.
• Strong understanding of software engineering best practices and agile workflows.