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
Skills Required
- 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
- Comfort working in ambiguous, fast-moving environments
- Ownership mindset and openness to experimentation and continuous improvement
- Experience with Python
- Experience with PyTorch or JAX
- Experience with ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
- Experience with cloud infrastructure
- Experience with ML or data pipelines and workflow orchestration
- Experience with GPU infrastructure and performance tooling
- Experience with vector databases and retrieval infrastructure
What We Do
Cygnify offers Talent Acquisition as a Service (TAaaS), providing a fully-managed team of experts, AI tools, and a candidate database on a flexible, month-to-month subscription model.
.jpeg)
