Machine Learning Engineer

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October 24

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Ensono

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Cloud Services • IT Services • Managed Services

Ensono is a managed service provider and expert technology advisor focused on enabling clients to navigate complex IT environments. Ensono offers services including mainframe-as-a-service, cloud migration, and IT infrastructure management, with a strong emphasis on modernizing legacy systems and optimizing IT operations. The company is recognized for its flexibility and allyship, providing businesses with the ability to adapt and evolve through solutions like Ensono Flex®. It specializes in both mainframe and cloud solutions, holding competency in AWS mainframe modernization and is a global Azure Expert MSP. Ensono acts as an ally, helping companies achieve better business outcomes through IT innovation and strategic technology advisement.

1001 - 5000 employees

📋 Description

• Model Deployment – Productionize machine learning models built by Data Scientists, ensuring they run reliably, securely, and at scale. • API & Service Development – Design APIs and services that expose model predictions to EnvisionOS, ServiceNow, and other enterprise systems. • Performance Optimization – Tune models for latency, throughput, and cost efficiency in real-time environments. • Feature Pipeline Integration – Collaborate with Data Engineers to ensure robust feature pipelines feed models consistently and with minimal drift. • Automation & Scaling – Use containers, orchestration, and CI/CD practices to automate deployment and monitoring of models. • Cross-functional Collaboration – Work with Ops, Data Science, and MLOps to ensure models deliver actionable, explainable outcomes that drive trust and adoption.

🎯 Requirements

• Strong programming skills in Python (must-have) plus C, C++, Java, Javascript for performance-critical applications. • Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. • Hands-on experience with Docker, Kubernetes, or other container orchestration tools. • Familiarity with Snowflake and data engineering workflows for integrating feature pipelines. • Experience deploying models in production and exposing them through REST APIs, Flask, or Streamlit. • Knowledge of SnowFlake is beneficial • Strong understanding of model optimization, hyperparameter tuning, and inference performance. • Experience working with ServiceNow or IT operations datasets is highly desirable.

🏖️ Benefits

• Flexible and remote work opportunities • Performance bonus • Training and development programs • Worldwide career opportunities • Community outreach and mentoring opportunities • Learning platforms • My Benefit system • Wellness Platform support from Virgin Pulse • Associate equity program • Life insurance • Lunch card • Study leave • One paid day off for charity events • Sabbatical • Extended parental leave • Rental or co-financing of office equipment • Referral bonus program