AI/ML Platform MLOps Engineer

About the role We are seeking a highly skilled AI/ML Platform MLOps Engineer to design, build, and operate a scalable AI/ML platform supporting the development and delivery of the LIAL product. This role focuses on enabling end-to-end machine learning lifecycle capabilities through robust platform engineering, automation, and cloud-native infrastructure. The ideal candidate combines strong cloud engineering, ML systems expertise, and software engineering discipline to deliver production-grade AI/ML capabilities with a security-first mindset. Key Responsibilities Cloud & Infrastructure Engineering Design, implement, and manage AI/ML platform infrastructure primarily on Google Cloud Platform (GCP), leveraging Vertex AI services. Build and operate ML pipelines using Vertex AI Pipelines, including orchestration of training, evaluation, and deployment workflows. Manage model registries, lineage tracking, and lifecycle governance. Implement monitoring and observability frameworks for model performance, data drift, and system health. Provision all infrastructure using Terraform (GCP provider) with: Remote state management Modular, reusable infrastructure patterns Integration into CI/CD pipelines Automation & HPC Integration Develop and maintain CI/CD pipelines using GitHub Actions, including: Secure authentication via Workload Identity Federation (WIF) Token exchange and secrets management Author and manage Slurm-based HPC workloads, including: GPU/resource allocation and scheduling Hybrid workflows combining cloud and HPC environments Implement automated workflows for training, validation, deployment, and retraining. Python Development & API Engineering Develop production-grade solutions using Python 3.10+ and ML libraries such as: TensorFlow, PyTorch, scikit-learn Utilize MLOps tooling including: Vertex AI MLflow (experiment tracking, model management) Design, build, test, and document REST APIs using: FastAPI or Flask OAuth 2.0 authentication OpenAPI/Swagger specifications Ensure API reliability, scalability, and security for integration with downstream applications. End-to-End ML Lifecycle Ownership Support the full ML lifecycle, including: Feature engineering and data preparation Experiment tracking and reproducibility Distributed and GPU-based training workflows Model evaluation, validation, and packaging Deploy models to Vertex AI Endpoints with scalable serving configurations. Implement production monitoring including: Model performance metrics Data drift detection Alerting and observability Enable automated retraining pipelines based on monitoring triggers and data changes. Ways of Working & Collaboration Operate autonomously with ownership of deliverables and platform components. Apply a security-first mindset across all aspects of platform design and implementation. Maintain strong planning, documentation, and reporting discipline. Collaborate effectively with: Data scientists Product teams (LIAL) Platform and DevOps engineers Continuously improve platform reliability, scalability, and developer experience. Required Qualifications Proven experience in MLOps / ML Platform Engineering in production environments Hands-on expertise with: GCP (Vertex AI) and cloud-native ML workflows Terraform (GCP provider) for infrastructure as code Strong experience with: Python (3.10+) ML frameworks (TensorFlow, PyTorch, scikit-learn) MLOps tools (MLflow, KFP SDK) Experience building secure, production-grade APIs using FastAPI or Flask Solid understanding of: CI/CD pipelines (GitHub Actions) Identity & access management (OAuth 2.0, WIF) Experience with Slurm and HPC environments, including GPU scheduling Strong knowledge of: ML lifecycle best practices Distributed training and model deployment patterns Preferred Qualifications Experience designing enterprise-scale ML platforms Familiarity with hybrid cloud/HPC architectures Knowledge of data governance, compliance, and model risk management Experience optimizing GPU workloads and cost efficiency on GCP Exposure to scientific, analytics, or data-intensive product environments Key Competencies Strong problem-solving and system design skills Excellent communication and stakeholder engagement High level of autonomy and accountability Attention to detail and commitment to quality Security-first engineering mindset Outcome for the Role This role will deliver a robust, scalable AI/ML platform enabling the LIAL product to rapidly develop, deploy, and operate machine learning models in production, with high reliability, traceability, and automation across the full lifecycle.