Realtech Services

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    Infographic showing various Software job openings at Realtech Services in the United States as of July 2026, with employment types broken down into 100% Contract. Highlights an 100% Physical job distribution.

    Hiring: ML-Ops Engineer at Concord, CA

    Realtech Services

    Concord, CA • On-site

    Contractor

    Posted 10 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.