MLOps / ML Platform Engineer
Role Overview Do you enjoy turning experimental machine learning work into dependable, production-ready systems? Are you comfortable owning infrastructure, automation, and operational excellence for AI workloads? We are seeking an MLOps Engineer who will enable smooth transitions from research prototypes to scalable, enterprise-level ML solutions. This role focuses on building and operating resilient ML platforms across a hybrid environment, ensuring models are deployed, monitored, and governed with consistency and security.Main Responsibilities ML Platform Automation Create and operate automated workflows that support model building, validation, deployment, and retraining using modern CI/CD and continuous training practices. Infrastructure Automation Define and manage cloud and on-premise resources using Infrastructure as Code approaches, primarily leveraging Terraform and shell-based automation across Azure, GCP, and local environments. Container-Based Workflows Enable standardized model packaging and scalable runtime environments through Docker images and Kubernetes-based orchestration. Collaboration with ML Teams Work closely with data scientists and ML engineers to convert experimental notebooks and models into stable, deployable services. Security, Compliance & Governance Establish and enforce security controls, access policies, and governance standards that protect data and models throughout their lifecycle. Data Platform Operations Support and maintain multiple data storage technologies—including relational databases, vector search engines, and graph-based systems—aligned with different ML use cases. Monitoring & Reliability Build observability solutions that provide visibility into model behavior, data quality, system health, and infrastructure performance. ML Tooling Ecosystem Integrate and maintain ML development platforms and libraries (such as Hugging Face) to streamline experimentation and deployment. Key Requirements Professional Background Hands-on experience in MLOps, DevOps, platform engineering, or similar roles focused on automation and infrastructure. Automation & Scripting Strong scripting skills in Python and shell languages (Bash, PowerShell) for building reliable automation. Containers & Orchestration Practical experience designing and operating Docker-based workloads and Kubernetes clusters. Cloud & IaC Expertise Experience working with cloud platforms—preferably GCP, with Azure as a plus—and significant hands-on use of Terraform. Security & Networking Knowledge Solid understanding of secure system design, encryption practices, identity and access management, and core networking concepts. Data Systems Exposure Familiarity with SQL databases as well as modern vector and graph data stores. Machine Learning Foundations Conceptual understanding of contemporary ML approaches, including LLMs, embeddings, and retrieval-augmented generation techniques. Nice to Have Advanced Python & ML Libraries Strong Python skills with exposure to ML frameworks such as PyTorch, TensorFlow, or Transformers. GCP ML Ecosystem Experience using Google Cloud ML services like Vertex AI or BigQuery ML. On-Premise & GPU Workloads Background in managing on-premise systems, particularly those supporting GPU-heavy ML workloads. Hybrid Cloud Operations Demonstrated experience designing or operating hybrid-cloud infrastructures. Azure IaC Tools Familiarity with Bicep for Azure resource management. Other Details This position offers a flexible working environment in a hybrid model and focuses on developing machine learning systems across various industries. Candidates should be prepared to work collaboratively with teams across different regions, ensuring reliability and innovation in ML deployment.
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