Desirable Skills:

· Google Cloud

· Azure Machine Learning (ML)

Role Requirements:

· Expertise in cloud platforms, ML engineering, data pipelines and CI/CD for deploying and managing machine learning solutions.

Cloud Platforms Services (Google Cloud)

· Google Cloud Platform (GCP) services: AI Platform (Vertex AI), Cloud Storage, BigQuery, Cloud Functions, Cloud PubSub, Cloud Build, Airflow, and Cloud Run.

· Element Platform visibility

ML Data Engineering

· Understanding of 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 using serverless dataproc.

CI/CD for ML (MLOps)

· Knowledge of CI/CD tools like Looper Pro and 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 Containerization

· Kubernetes (GKE) and Docker for containerization and orchestration.

· Good to have Helm charts and YAML for Kubernetes deployments.

Monitoring Observability

· Cloud Monitoring, Cloud Logging, Prometheus and Grafana for monitoring and alerting.

· Model performance monitoring with Vertex AI Model Monitoring.

Security Compliance

· Understanding of VPC, firewall rules, and service accounts.

· Managing secrets using Secret Manager.

Data Science

· Must understand general data science methods and the development life cycle.

· An MLOps Engineer responsible for building, automating, and managing scalable machine learning pipelines and deployments on Google Cloud Platform.

Experience Required: 6-8