Machine Learning / ML Ops Engineer

Job DescriptionJoin our dynamic team in San Francisco as a Machine Learning / ML Ops Engineer on a contractual basis. This is an exciting opportunity to contribute to cutting-edge machine learning projects, leveraging your expertise to build, deploy, and maintain robust ML systems. You will play a crucial role in streamlining our ML workflows and ensuring the scalability and reliability of our machine learning solutions.Position: ML Ops EngineerLocation: San Francisco, CA Bay area (Hybrid)Duration : Long term contractMust have valid work visa to be eligibleMode of work – Hybrid(Onsite interview required) OverviewTachyon 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 deploymentQualifications• 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.All candidate submissions should come with valid documents and ID proof.