{"id":2346937,"url":"https://alion.io/job/cygnify-platform-engineer-machine-learning","title":"Platform Engineer (Machine Learning)","company":{"id":4418503,"name":"Cygnify","domain":"cygnify.io","url":"https://alion.io/company/cygnify","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":23,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"AI Agents","optional":true},{"name":"JAX","optional":true},{"name":"LLM","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"SGLang","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-08-19T11:21:13Z","employer_posted_date":"2026-08-19","last_verified_at":"2026-10-13T07:57:24Z","board_verified":true,"closed_at":null,"days_open":54,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":54},"description":"About the Company\nWe 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.\nA1'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.\nBJAK is the largest insurance platform in Southeast Asia with presence in Japan, United Kingdom and growing.\nRole Summary\nAs 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.\nResponsibilities\nBuild and operate the ML infrastructure and platforms powering A1’s AI products\n\nDesign systems for model training, evaluation, deployment, inference, and experimentation\n\nBuild and optimise model serving and inference infrastructure for high-throughput and low-latency workloads\n\nImprove reliability, scalability, latency, and cost efficiency of AI systems\n\nDevelop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement\n\nBuild platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster\n\nDevelop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions\n\nBuild production observability, monitoring, tracing, and alerting for AI/ML workloads\n\nImprove AI systems across reliability, scalability, latency, throughput, and cost\n\nIdentify bottlenecks across the ML stack and continuously improve system performance\n\nWork closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure\n\nKey Performance Indicators\nAI infrastructure reliably supports production workloads at scale\n\nModels can be trained, evaluated, deployed, and improved efficiently\n\nInference systems deliver strong latency, throughput, reliability, and cost efficiency\n\nML pipelines are reproducible, observable, maintainable, and robust\n\nModel and infrastructure regressions are detected quickly and diagnosed efficiently\n\nCommon ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product\n\nThe AI stack can evolve rapidly as new models, architectures, and inference techniques emerge\n\nOur Ideal Candidate\nHas strong software engineering fundamentals and experience building production systems\n\nHas experience building ML infrastructure, platforms, or production machine learning systems\n\nHas experience with model deployment, inference, evaluation, or data pipelines\n\nHas strong understanding of distributed systems and system reliability\n\nAble to write clean, maintainable, production-quality code\n\nComfortable working in ambiguous, fast-moving environments\n\nTakes ownership, open to experimentation and continuous improvement\n\nHas the following Tech Stack:\nPython\n\nPyTorch / JAX\n\nLLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM\n\nCloud infrastructure\n\nDistributed systems\n\nML/data pipelines and workflow orchestration\n\nGPU infrastructure and performance tooling\n\nVector databases and retrieval 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