Machine Learning Operations

ML Ops Engineer

At Ensono, our Purpose is to be a relentless ally, disrupting the status quo and unleashing our clients to Do Great Things! We enable our clients to achieve key business outcomes that reshape how our world runs. As an expert technology adviser and managed service provider with cross-platform certifications, Ensono empowers our clients to keep up with continuous change and embrace innovation.

We can Do Great Things because we have great Associates. The Ensono Core Values unify our diverse talents and are woven into how we do business. These five traits are the key to achieving our purpose.

HONESTY, RELIABILITY, COLLABORATION, CURIOSITY, PASSION

At Ensono, we are building the future of managed services with AI, automation, and human expertise working together. Our Envision Operating System is designed to orchestrate predictive, zero-touch operations across mainframe, distributed, and cloud environments.

As an MLOps Engineer, you’ll be the guardian of the machine learning lifecycle—making sure models built by Data Scientists and deployed by ML Engineers continue to perform, scale, and deliver business value in production. From monitoring drift to automating retraining, you’ll own the workflows that ensure AI doesn’t just launch, but lasts.

This role is perfect for engineers who thrive in the space between data science, DevOps, and operations. You’ll build pipelines, logging frameworks, and monitoring systems that make AI reliable and explainable. Your work ensures that our incident predictions, anomaly detections, and optimization models are not just powerful, but trusted by Ops teams and cost-effective for the business.

If you’re a builder who loves creating end-to-end systems that keep AI alive and useful in the real world, this is the role for you.

What You Will Do:

• Lifecycle Automation – Build workflows for retraining, validation, and redeployment of models to keep them current and reliable.
• Monitoring & Observability – Develop systems to track model performance, drift, cost, and reliability in production environments.
• Logging & Explainability – Ensure every prediction is traceable, explainable, and auditable to build trust with Ops and clients.
• Performance & Cost Optimization – Identify opportunities to improve inference performance while reducing compute/storage costs.
• Collaboration with AI/ML Teams – Partner with Data Scientists and ML Engineers to translate model requirements into production-ready, maintainable workflows.
• Tooling & Platform Management – Standardize and scale the use of ML platforms, frameworks, and monitoring tools across the enterprise.
  
  
  

• Strong programming skills in Python (must-have) with knowledge of C++ or Java as a plus.
• Experience with Docker, Kubernetes, or similar orchestration platforms.
• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and their production lifecycle.
• Hands-on experience with MLOps platforms and tools (MLflow, Kubeflow, SageMaker, etc.).
• Strong background in monitoring, logging, and performance tuning of production ML systems.
• Exposure to Snowflake, ServiceNow data flows, and enterprise IT operations datasets.
• Knowledge of cost optimization strategies for AI/ML workloads.