AI Platform Engineer – ML Ops
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Job Description
AI Platform Engineer at Toku managing MLOps pipelines and deploying AI systems. Focusing on cloud-native AI workloads with strong collaboration across engineering teams.
Responsibilities:
- Design, improve, and operate MLOps pipelines for training, deploying, and managing ML models in production.
- Build and maintain CI/CD-style workflows for model packaging, versioning, and deployment across environments.
- Operate and optimise AWS-based infrastructure for AI workloads, including compute, storage, and networking components.
- Manage GPU-enabled workloads, addressing scalability, reliability, and cost-efficiency for high-load AI applications.
- Implement monitoring and alerting for deployed models, focusing on system health, performance, and operational stability.
- Own and evolve shared tooling such as MLflow, Docker-based workflows, and deployment frameworks to improve developer productivity.
- Work closely with infrastructure, SRE, and engineering teams to align AI platform practices with broader system standards.
- Support live AI services by diagnosing deployment, scaling, and infrastructure-related issues impacting AI features.
- Ensure reproducibility, traceability, and governance across the full ML lifecycle, from experimentation to production.
Requirements:
- Hands-on experience building and operating MLOps pipelines for production ML systems.
- Strong experience with AWS services used for AI workloads, including EC2, ECS, and SageMaker.
- Practical experience with Docker and container-based deployment of ML workloads.
- Experience with MLflow or similar tools for experiment tracking, model versioning, and lifecycle management.
- Experience managing GPU-based workloads and addressing performance and cost challenges at scale.
- Strong understanding of cloud infrastructure concepts as they apply to ML systems.
- Ability to work with Python-based ML codebases to support deployment and lifecycle needs.
- Working familiarity with LLMs, NLP models, and applied ML concepts sufficient to support deployment and monitoring (without owning core model development).
- Proven experience supporting live, production ML systems with real customer impact.
- Ability to work cross-functionally with applied AI engineers, backend engineers, and infra teams.
Benefits:
- Training and Development
- Discretionary Yearly Bonus & Salary Review
- Healthcare Coverage based on location
- 20 days Paid Annual Leave (excluding Bank holidays)
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