ml ops

1 day ago

Hyderabad, Telangana, India HCLSoftware Full time ₹ 12,00,000 - ₹ 24,00,000 per year

We are looking for a skilled
MLOps Engineer
to join our team and help us build, deploy, and maintain robust and scalable machine learning systems. You will be responsible for the full lifecycle of our ML pipelines, from data ingestion to model serving. This is a hands-on role where you will design and implement automated workflows, ensure data quality, and manage model deployments in a production environment.

Responsibilities

  • Data and Feature Pipelines:
    Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like
    Kubeflow Pipelines
    and
    Vertex AI Pipelines
    .
  • Feature Engineering:
    Implement and containerize feature engineering logic for diverse datasets, ensuring reusability and scalability.
  • Data Validation:
    Integrate and manage data validation processes, including leveraging advanced techniques like
    AI Agents
    and the
    Generative Language API
    to automatically detect and remediate data quality issues.
  • Model Training and Experimentation:
  • Set up and maintain automated continuous training (
    CT
    ) pipelines using
    Vertex AI Pipelines
    (Schedules) and
    Cloud Scheduler
    .
  • Implement
    experiment tracking
    to log and compare model parameters, metrics, and artifacts.
  • Configure and execute
    Hyperparameter Tuning
    jobs using
    Vertex AI Training
    to optimize model performance.
  • Model Management:
    Establish a robust
    Model Versioning
    system to manage and store model artifacts securely in a centralized repository (
    Cloud Storage
    ).
  • Deployment and Serving:
  • Containerize ML models and their dependencies using
    Docker
    and manage images with
    Artifact Registry
    .
  • Build and maintain
    CI/CD
    workflows for ML models, ensuring seamless and automated deployment.
  • Configure and manage low-latency production serving environments using
    Vertex AI Endpoints
    for real-time inference.

Qualifications

  • Strong experience with Google Cloud Platform (
    GCP
    ) services, specifically in the MLOps and ML domain (
    Vertex AI, Kubeflow, Cloud Storage, Artifact Registry
    ).
  • Proven ability to design and implement end-to-end ML pipelines for data management, model training, and deployment.
  • Hands-on experience with containerization technologies like
    Docker
    .
  • Familiarity with CI/CD practices and pipeline automation.
  • Knowledge of ML frameworks like
    TensorFlow
    , and experience with experiment tracking and hyperparameter tuning.
  • Excellent problem-solving skills and a strong understanding of the ML lifecycle.
  • Experience with the Generative Language API (
    Gemini model
    ) or other AI Agent integrations is a plus.

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