ML Platform Tech Lead — Production-Grade LLM Systems

Avomind; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Avomind is seeking a Technical Lead, Machine Learning to translate research into scalable, production-ready ML systems. This role sits at the intersection of research, infrastructure, and product, ensuring models are trainable, deployable, observable, and optimized for real-world performance.

You will partner with research, engineering, and product teams to deliver robust ML infrastructure and optimal inference solutions.

Qualifications

• Proven experience building and deploying production-grade ML systems.
• Strong understanding of large language models and failure modes.
• Experience developing scalable ML infrastructure and training pipelines.
• Solid software engineering with production-quality code.
• Ability to balance latency, reliability, scalability and cost.
• Independent ownership of technical initiatives from design to deployment.
• Excellent communication and cross-functional collaboration.

Responsibilities

• Lead end-to-end ML system development including data pipelines, training workflows, and deployment.
• Fine-tune and optimize models using modern techniques and distillation methods.
• Design scalable inference systems prioritizing latency, cost efficiency, and reliability.
• Develop data pipelines for synthetic and real-world data.
• Build evaluation frameworks for performance, safety and bias.
• Improve production deployments via GPU utilization and optimization.
• Collaborate with backend, desktop and mobile teams to integrate ML.
• Drive rapid iteration and data-driven optimization of systems.

Skills

Production ML systems

LLMs

ML infra

Software engineering

Latency & cost trade-offs

Ownership mindset

Cross-functional collaboration

Tools

Python

PyTorch

JAX

GPU training & inference

Job description

Avomind is seeking a Technical Lead, Machine Learning to translate research into scalable, production-ready ML systems. This role sits at the intersection of research, infrastructure, and product, ensuring models are trainable, deployable, observable, and optimized for real-world performance.

You will partner with research, engineering, and product teams to deliver robust ML infrastructure and optimal inference solutions.