ML Engineer

Key Responsibilities 2. ML & Data Engineering 3. CI/CD for ML (MLOps) 4. Automation & Scripting 5. DevOps & Containerization 6. Monitoring & Observability 7. Security & Compliance 8. Data Science Fundamentals

• Cloud Platform Services (GCP)
• Hands-on experience with GCP services: Vertex AI, BigQuery, Cloud Storage, Cloud Functions, Cloud Pub/Sub, Cloud Build, Cloud Run, Airflow.
• Platform visibility with Element Platform tools (if applicable).
• Solid understanding of ML workflows and LLM concepts (training, validation, hyperparameter tuning).
• Hands-on with TensorFlow, Keras, PyTorch, scikit-learn.
• Build ETL pipelines using PySpark and Scala on Dataproc (serverless).
• CI/CD tools like Looper, Jenkins for ML pipeline automation.
• Experience in Vertex AI Pipelines, model versioning, and continuous deployment.
• Strong skills in Python, Bash, and SQL.
• Automate ML workflows and processes.
• Experience with Kubernetes (GKE) and Docker.
• Helm charts and YAML for Kubernetes deployment (good to have).
• Use tools like Cloud Monitoring, Cloud Logging, Prometheus, Grafana.
• Monitor model performance with Vertex AI Model Monitoring.
• Knowledge of VPC, firewall rules, service accounts, and Secret Manager.
• Understanding of data science lifecycle, methodologies, and best practices.

Essential Skills

• GCP Services: Vertex AI, BigQuery, Cloud Functions, Pub/Sub, Cloud Run, Cloud Build, Airflow
• ML Frameworks: TensorFlow, Keras, PyTorch, scikit-learn
• Programming: Python, Bash, SQL
• CI/CD Tools: Looper, Jenkins
• MLOps Practices and Pipelines