Machine Learning Architect

5 days ago

Hyderabad, Telangana, India Grid Dynamics Full time
Lead Machine Learning Engineer

About the Role:

We are looking for an experienced Lead Machine Learning Engineer with 7-14 years of experience to lead the design, deployment, and maintenance of end-to-end machine learning pipelines using Azure ML pipeline components, Azure Databricks, and Azure DevOps. The ideal candidate will have a strong background in building infrastructure for classic data science models and large language models (LLMs) / small LLMs (SLMs), as well as orchestration and CI/CD for machine learning.

Key Responsibilities:

  • Design and deploy end-to-end machine learning pipelines using Azure ML pipeline components, Azure Databricks, and Azure DevOps.
  • Build infrastructure for classic data science models and/or LLM/SLM solutions.
  • Develop and maintain containerized machine learning solutions using Docker and Kubernetes.
  • Implement model and data versioning using MLflow and DVC.
  • Ensure MLSecOps security in the context of MLOps.
  • Maintain Infrastructure as Code using Terraform and CloudFormation.

Expected Outcomes:

  • List of identified areas for improvements for existing project.
  • Recommendations on MLOps practice, MLSecOps, and sample templates/pipelines.
  • Infrastructure recommendations for Classic DS models and LLM solution (fine-tuning SLM by domain).
  • Data strategy recommendation: JIT access to data for training with security guardrails.

Requirements:

  • 2+ years of experience with Azure (ML pipeline components), Azure Databricks, Azure DevOps.
  • Proven experience in design and deployment of end-to-end machine learning pipelines.
  • Experience with building infrastructure for classic data science models and/or LLM/SLM solutions.
  • 2+ years of experience with orchestration (e.g., Kubeflow, Airflow, Azure Data Factory) and CI/CD for machine learning.
  • Experience deploying containerized machine learning solutions (e.g., Docker/Kubernetes).
  • Knowledge of model and data versioning (e.g., MLflow, DVC).
  • Knowledge of MLSecOps (security in the context of MLOps).
  • Experience with Infrastructure as a Code (e.g., Terraform, CloudFormation).

Nice-to-Haves:

  • Knowledge of MLOps for LLM/SLM.
  • Experience in machine learning system/architecture design (load balancing, caching, failover).
  • Knowledge in building scalable, resilient machine learning architectures.
  • Cross-team collaboration experience (data science, engineering, DevOps).
  • Experience with monitoring/logging for production models (e.g., Prometheus, Grafana, ELK stack).

Salary: ₹1,500,000 - ₹3,000,000 per annum.

Work Location: Flexible; willing to relocate or work remotely.

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