Responsibilities: - • Model Deployment: Design and implement pipelines for deploying machine learning models into production using GCP services such as AI Platform, Vertex AI, or Cloud Run, Cloud Composer ensuring high availability and performance. • Infrastructure Management: Build and maintain scalable GCP-based infrastructure using services like Google Compute Engine, Google Kubernetes Engine (GKE), and Cloud Storage to support model training, deployment, and inference. • Automation: Develop automated workflows for data ingestion, model training, validation, and deployment using GCP tools like Cloud Composer, and CI/CD pipelines integrated with GitLab and Bitbucket Repositories. • Monitoring and Maintenance: Implement monitoring solutions using Google Cloud Monitoring and Logging to track model performance, data drift, and system health, and take corrective actions as needed. • Collaboration: Work closely with data scientists, Data engineers, Infrastructure and DevOps teams to streamline the ML lifecycle and ensure alignment with business objectives. • Versioning and Reproducibility: Manage versioning of datasets, models, and code using GCP tools like Artifact Registry or Cloud Storage to ensure reproducibility and traceability of machine learning experiments. • Optimization: Optimize model performance and resource utilization on GCP, leveraging containerization with Docker and GKE, and utilizing cost-efficient resources like preemptible VMs or Cloud TPU/GPU. • Security and Compliance: Ensure ML systems comply with data privacy regulations (e.g., GDPR, CCPA) using GCP’s security tools like Cloud IAM, VPC Service Controls, and Data Loss Prevention (DLP). • Tooling: Integrate GCP-native tools (e.g., Vertex AI, Cloud composer) and open-source MLOps frameworks (e.g., MLflow, Kubeflow) to support the ML lifecycle. • Enable successful project delivery and customer satisfaction. • Drive project and technology goals in compliance with organizational objectives. Experience: - • 8+ Years Location: - • Nashville TN; Malvern, PA Educational Qualifications: - • Engineering Degree – BE/ME/BTech/MTech/BSc/MSc. • Technical certification in multiple technologies is desirable. Skills: - Mandatory skills Technical Skills: • Proficiency in programming languages such as Python. • Expertise in GCP services, including Vertex AI, Google Kubernetes Engine (GKE), Cloud Run, BigQuery, Cloud Storage, and Cloud Composer, Data proc or PySpark and managed Airflow. • Experience with infrastructure-as-code - Terraform. • Familiarity with containerization (Docker, GKE) and CI/CD pipelines, GitLab and Bitbucket. • Knowledge of ML frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps tools compatible with GCP (MLflow, Kubeflow) and Gen AI RAG applications. • Understanding of data engineering concepts, including ETL pipelines with BigQuery and Dataflow, Dataproc - Pyspark. Soft Skills: • Strong problem-solving and analytical skills. • Excellent communication and collaboration abilities. • Ability to work in a fast-paced, cross-functional environment Good to have skills: - • Experience with large-scale distributed ML systems on GCP, such as Vertex AI Pipelines or Kubeflow on GKE, Feature Store. • Exposure to Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) applications and deployment strategies. • Familiarity with GCP’s model monitoring tools and techniques for detecting data drift or model degradation. • Knowledge of microservices architecture and API development using Cloud Endpoints or Cloud Functions. • Google Cloud Professional certifications (e.g., Professional Machine Learning Engineer, Professional Cloud Architect).