Ensono is transforming into a software-first Managed Services Provider, utilizing AI/ML and automation for predictive, zero-touch operations. The Envision Operating System is key to this shift, integrating data, intelligence, and automation across various environments. The Machine Learning Engineer will be responsible for operationalizing AI/ML models to power real-time operations, reduce downtime, and drive business outcomes for clients.
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.
Responsibilities
- Productionize machine learning models built by Data Scientists, ensuring they run reliably, securely, and at scale.
- Design APIs and services that expose model predictions to EnvisionOS, ServiceNow, and other enterprise systems.
- Tune models for latency, throughput, and cost efficiency in real-time environments.
- Collaborate with Data Engineers to ensure robust feature pipelines feed models consistently and with minimal drift.
- Use containers, orchestration, and CI/CD practices to automate deployment and monitoring of models.
- Work with Ops, Data Science, and MLOps to ensure models deliver actionable, explainable outcomes that drive trust and adoption.
Other
- HONESTY, RELIABILITY, COLLABORATION, CURIOSITY, PASSION
- Get Stuff Done – You take pride in moving models out of slides and into production.
- Builder at Heart – You see APIs, services, and pipelines as products that should be reliable, elegant, and scalable.
- Impact-Oriented – You measure success by uptime improvements, cost savings, and real-world adoption of AI-driven workflows.
- Collaborative Engineer – You bridge the gap between Data Scientists and Ops teams, speaking both “ML” and “production.”
