ML Ops Engineer

Position: ML Ops Engineer

Location: Bay area (Hybrid)

Duration : Long term

Mode of work – Hybrid

Overview

Tachyon Predictive AI team seeking a ML Ops Engineer to drive the full lifecycle of machine learning solutions.

Key Responsibilities

•            Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.

•            Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).

•            Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.

•            Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)

•            Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs

•            Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment

Qualifications

•            10+ Years of professional experience in Software Engineering & 3+ Years in AIML, Machine Learning Model Operations.

•            Strong proficiency in Java and  Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).

•            Experience with cloud platforms and containerization (Docker, Kubernetes).

•            Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.

•            Solid understanding of software engineering principles and DevOps practices.

•            Ability to communicate complex technical concepts to non-technical stakeholders.