Job Description
Your future duties and responsibilities
We are seeking a high-caliber DevOps & MLOps Engineer to bridge the gap between robust infrastructure and cutting-edge Artificial Intelligence. In this role, you won't just be managing pipelines; you will be architecting the foundation for Generative AI and Machine Learning at scale.
You will be responsible for the end-to-end lifecycle of our applicationsfrom standard microservices to complex Large Language Models (LLMs)—ensuring they are scalable, reproducible, and secure within the Google Cloud Platform (GCP) ecosystem.
Key Responsibilities :
Infrastructure & Orchestration: Design and maintain scalable infrastructure using Terraform and manage containerized workloads via Docker and Kubernetes (GKE).
CI/CD Excellence: Build and optimize automated deployment pipelines using Cloud Build to ensure rapid, reliable delivery of both code and models.
MLOps Mastery: Implement Agent Lifecycle Management and governance frameworks. Manage Feature Stores and ensure total reproducibility of ML experiments and production runs.
GenAI Deployment: Architect specialized environments for LLM Deployment & Scaling, specifically managing high-performance GPU/TPU resource allocation and optimization.
Monitoring & Governance: Establish rigorous Model Monitoring and observability patterns using GCP's operations suite to track performance, drift, and system health.
Data Engineering Support: Collaborate on data pipelines using BigQuery, Dataflow, and Dataproc to ensure seamless data flow for ML training and inference.