CYGNIFY PTE. LTD.
Machine Learning Platform Engineer
Visa Source Listed
📍🇸🇬 Singapore📅14h ago
S$72,000—S$1,20,000/ year
≈ ₹45L — ₹75L per year
Machine LearningModel ServingAiDistributed ProcessingVector SearchTechnology InfrastructureData PipelineGPUTools DevelopmentModel EvaluationModel Trainingautomated monitoringPyTorchCloud InfrastructurePython
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Job Description
Company Profile
We are partnering with Agentic Intelligence (AI) Development Start-up with 100% remote work arrangement. Substantial investment funding from well known Fintech organization and regionally based in Singapore, Malaysia, Hong Kong, UK & USA, currently rebranding and focused on building AI applications for everyday consumers.
The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product.
The Role
As an ML Platform Engineer, you will build the infrastructure and systems that power 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.
What you'll do:
- Build and operate the ML infrastructure and platforms powering company's AI products
- Design systems for model training, evaluation, deployment, inference, and experimentation
- Build and optimize 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
Ideal Experience
- 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 improvements
Tech Stack
- Python
- PyTorch / JAX
- LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
- Cloud infrastructure
- Distributed systems
- ML/data pipelines and workflow orchestration
- GPU infrastructure and performance tooling
- Vector databases and retrieval infrastructure
UEN: 202329526G
EA Licence: 23S2061