Job Title: ML-Ops Engineer Location: Concord, CA (Onsite) Duration: Long-Term Contract Interview Process: Client Round - In-person (Lets Target only locals and willing to go for in-person interview ...
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Contractor
Posted 26 days ago
Job description
Job Title: ML-Ops Engineer
Location: Concord, CA (Onsite)
Duration: Long-Term Contract
Interview Process: Client Round – In-person (Lets Target only locals and willing to go for in-person interview at Client’s location)
Overview:
- 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 Auto ML tools (e.g., Vertex AI Auto ML, 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.