ML Ops Engineer

Mphasis; Concord (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Tachyon Cortex is seeking an ML Ops Engineer to drive end-to-end ML solution lifecycle. You will design and maintain pipelines using MLflow, Kubeflow, or Vertex AI and collaborate with cross-functional teams.

The role emphasizes cloud deployment, CI/CD for models, monitoring, governance, and low-latency scoring. This position requires strong software engineering with DevOps practices and the ability to communicate complex concepts.

Qualifications

• 10+ years of software engineering experience with 3+ years in AIML and MLOps.
• Experience building ML pipelines and model deployment.
• Strong communication to non-technical stakeholders.

Responsibilities

• Develop and maintain ML pipelines with MLflow, Kubeflow, or Vertex AI.
• Automate training, testing, deployment, and monitoring in the cloud.
• Implement CI/CD workflows for model lifecycle management.
• Monitor model performance and ensure governance and explainability.
• Collaborate to provision containerized environments and support scoring via low-latency APIs.
• Leverage AutoML tools for low-code model development and rapid deployment.

Skills

Java

Python

SQL

ML libraries

Docker

Kubernetes

Airflow

Spark

DevOps

Communication

Tools

Docker

Kubernetes

Airflow

Spark

Job description

Tachyon Cortex Machine Learning 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.

Required Skills

• 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.