Machine Learning Platform Engineer

About the role

As an ML Platform Engineer, you will build the infrastructure and systems that power ActAI's AI capabilities. You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement. You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems.

Responsibilities

• Build and operate the ML infrastructure and platforms powering AI products
• Design systems for model training, evaluation, deployment, inference, and experimentation
• Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads
• Improve reliability, scalability, latency, and cost efficiency of AI systems
• Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement
• Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster
• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions
• Build production observability, monitoring, tracing, and alerting for AI/ML workloads

Required skills

• Python
• PyTorch
• JAX
• vLLM
• SGLang
• TensorRT-LLM
• Cloud infrastructure
• Distributed systems
• ML pipelines
• Workflow orchestration
• GPU infrastructure
• Vector databases

Qualifications

• Strong software engineering fundamentals and experience building production systems
• Experience building ML infrastructure, platforms, or production machine learning systems
• Experience with model deployment, inference, evaluation, or data pipelines
• Strong understanding of distributed systems and system reliability

About the Company

ActAI aims to build proactive applications for anyone in the world, bringing intelligence to conversations, errands, organising and workflows with minimal to no prompting. The product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion.