Data Science Engineer
2 weeks ago
Job Overview:
We are seeking a seasoned Lead Data Scientist with 7-14 years of experience to lead the design, deployment, and maintenance of end-to-end data science pipelines using Azure ML pipeline components, Azure Databricks, and Azure DevOps. The successful candidate will have a strong background in building infrastructure for classic machine learning models and large language models (LLMs) / small LLMs (SLMs), as well as orchestration and CI/CD for data science.
Responsibilities:
- Design and deploy end-to-end data science pipelines using Azure ML pipeline components, Azure Databricks, and Azure DevOps.
- Build infrastructure for classic machine learning models and/or LLM/SLM solutions.
- Develop and maintain containerized data science solutions using Docker and Kubernetes.
- Implement model and data versioning using MLflow and DVC.
- Evaluate MLSecOps security in the context of MLOps.
- Maintain Infrastructure as Code using Terraform and CloudFormation.
Deliverables:
- List of identified areas for improvements for existing projects.
- 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 data science pipelines.
- Experience with building infrastructure for classic machine learning models and/or LLM/SLM solutions.
- 2+ years of experience with orchestration (e.g., Kubeflow, Airflow, Azure Data Factory) and CI/CD for data science.
- Experience deploying containerized data science 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 data science system/architecture design (load balancing, caching, failover).
- Knowledge in building scalable, resilient data science architectures.
- Cross-team collaboration experience (data science, engineering, DevOps).
- Experience with monitoring/logging for production models (e.g., Prometheus, Grafana, ELK stack).
Salary Range: ₹1,500,000 - ₹3,000,000 per annum.
Location: Grid Dynamics' office location is not specified in this job posting, but candidates should be willing to relocate or work remotely.
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