Senior Machine Learning Engineer

Responsibilities

Apply expertise in cloud platforms, ML engineering, data pipelines, and CI/CD for deploying and managing machine learning solutions. Work with Google Cloud Platform (GCP) services: AI Platform (Vertex AI), Cloud Storage, BigQuery, Cloud Functions, Cloud Pub/Sub, Cloud Build, Airflow, and Cloud Run. Understand ML concepts and LLMs (training, validation, hyperparameter tuning, evaluation). Experience with TensorFlow, Keras, PyTorch, and scikit-learn; data preprocessing, ETL, and data pipelines using PySpark and Scala on serverless Dataproc. CI/CD for ML (MLOps): Knowledge of CI/CD tools including Jenkins. Model versioning, continuous training, and deployment using Vertex AI pipelines. Automation scripting: Strong programming skills in Python, Bash, and SQL; automation of workflows and ML pipelines. DevOps: Kubernetes (GKE) and Docker for containerization and orchestration; familiarity with Helm charts and YAML for Kubernetes deployments. Monitoring and observability: Cloud Monitoring, Cloud Logging, Prometheus, and Grafana for monitoring and alerting; model performance monitoring with Vertex AI Model Monitoring. Security and compliance: Understanding of VPC, firewall rules, and service accounts. Data Science: Must understand general data science methods and the development life cycle; contribute as an ML Ops Engineer responsible for building, automating, and managing scalable machine learning pipelines and deployments on Google Cloud Platform.

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