Platform Engineer (Machine Learning)

Cygnify
Singapore
On-site

Who this role is best for

Strong fit for mid-level ML infrastructure engineers who design scalable systems and collaborate across AI, research, and product teams in Singapore.

Best fit for

  • Candidates with mid-level experience in building ML infrastructure for AI-native applications
    — “build the infrastructure and systems that power A1's AI capabilities”
  • Professionals adept at optimizing GPU infrastructure and handling high-throughput workloads
    — “GPU infrastructure and performance tooling”
  • Individuals who can translate evolving model requirements into robust production systems
    — “turn evolving model requirements into production-ready infrastructure”

Things to consider

  • Requires adaptability to rapidly changing AI technologies and architectures
    — “The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge”
  • Collaboration spans multiple specialized teams with overlapping priorities
    — “work closely with AI engineers, researchers, and product engineers”

How to stand out

  • Highlight experience with LLM serving frameworks like vLLM or TensorRT-LLM
    — “LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM”
  • Demonstrate pipeline development skills with reproducible and observable ML workflows
    — “Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement”
  • Showcase observability system design for AI/ML workloads in production environments
    — “Build production observability, monitoring, tracing, and alerting for AI/ML workloads”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build and operate ML infrastructure
  • design systems for model training and deployment
  • build and optimize model serving infrastructure
  • develop reliable ML pipelines
  • build production observability
Typical background
software engineeringAI engineeringML platform engineering

Skills & requirements

Required

ML InfrastructureModel TrainingDeploymentInferenceObservabilityPlatform EngineeringAI SystemsData PreparationModel EvaluationProduction Observability

Preferred

Hyperscale Data Center OperationsModular Data Center Design

Stack & domain

PythonPyTorchJAXVllmSglangTensorrt-llmCloud InfrastructureDistributed SystemsMl/data PipelinesWorkflow OrchestrationGPU InfrastructurePerformance ToolingVector DatabasesProblem SolvingTeamworkCommunicationAdaptabilityOwnershipExperimentationMachine LearningAIInfrastructureProduction Systems

About the role

Original posting from Cygnify via Ashby

About the Company

We are working with A1, a company incubated and backed by BJAK, whose mission is to build the next generation of AI-native applications that fundamentally change how people communicate and get things done. A1's first application, AI Email Triage, reimagines email by moving users from reading and writing emails to learning from and approving AI-completed work-- making it efficient, smart and delightful.

A1's core capabilities are Agentic AI - AI that can reason through multi-step workflows and use external tools to complete tasks; Permission-Based Actions - AI that always asks for approval before taking actions such as sending emails or updating your calendar, keeping users in control; Context & Memory - AI remembers user preferences and past context to deliver increasingly personalised, accurate, and consistent assistance over time.

BJAK is the largest insurance platform in Southeast Asia with presence in Japan, United Kingdom and growing.

Role Summary

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.

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

Key Performance Indicators

  • AI infrastructure reliably supports production workloads at scale
  • Models can be trained, evaluated, deployed, and improved efficiently
  • Inference systems deliver strong latency, throughput, reliability, and cost efficiency
  • ML pipelines are reproducible, observable, maintainable, and robust
  • Model and infrastructure regressions are detected quickly and diagnosed efficiently
  • Common ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product
  • The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge

Our Ideal Candidate

  • Has strong software engineering fundamentals and experience building production systems
  • Has experience building ML infrastructure, platforms, or production machine learning systems
  • Has experience with model deployment, inference, evaluation, or data pipelines
  • Has strong understanding of distributed systems and system reliability
  • Able to write clean, maintainable, production-quality code
  • Comfortable working in ambiguous, fast-moving environments
  • Takes ownership, open to experimentation and continuous improvement
  • Has the following 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

Source: Cygnify careers (Ashby)

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