Machine Learning Platform Engineer

📋 Job Overview

As an ML Platform Engineer, you will build the infrastructure and systems that power A1'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. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.

🏢 About Cygnify

A1 is a company focused on building AI capabilities and products. The role involves working on the ML platform that supports these AI products.

🎯 The Role

You will be responsible for building and operating the ML infrastructure and platforms that power A1’s AI products. This includes designing systems for model training, evaluation, deployment, inference, and experimentation, as well as building and optimizing model serving and inference infrastructure for high-throughput and low-latency workloads.

✅ Key Responsibilities

• Build and operate the ML infrastructure and platforms powering A1’s 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
• Improve AI systems across reliability, scalability, latency, throughput, and cost
• Identify bottlenecks across the ML stack and continuously improve system performance
• Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure

📌 Required 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
• Ability to write clean, maintainable, production-quality code
• Comfortable working in ambiguous, fast-moving environments
• Bias toward ownership, experimentation, and continuous improvement

⭐ Desirable Experience

• Experience with Python, PyTorch / JAX
• Experience with LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
• Experience with cloud infrastructure, distributed systems, ML/data pipelines and workflow orchestration, GPU infrastructure and performance tooling, and vector databases and retrieval infrastructure

🎁 Benefits

Competitive

🛂 Visa & Eligibility

Not specified