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.