Sr ML Engineer

Responsibilities:

• Build and manage end-to-end ML pipelines using Vertex AI Pipelines, Airflow, or similar orchestrators.
• Implement ML models using TensorFlow, PyTorch, or other frameworks; monitor model performance in production.
• Manage Python environments using venv, pip, poetry, and containerized deployments with Docker.
• Design and implement distributed data pipelines with Apache Spark, Beam, or Flink.
• Develop data ingestion workflows from databases and API-based sources using GCP tools.
• Optimize pipelines and clusters for performance, cost, and scalability.
• Perform feature engineering, data engineering, and ML model evaluation.
• Develop and maintain CI/CD pipelines, deployment strategies, and infrastructure-as-code using Terraform.
• Design data architecture considering storage and compute options for efficient BI and analytics use cases.
• Optional / Good to have: Kubernetes, Vector Databases (e.g., Qdrant), and LLM experience (embeddings, RAG, agents).