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.

Skills
Python, FastAPI, Flask, REST API, Git, GCP, CI/CD