Full-timeTech & engineering· Posted 30 Jul

Senior Machine Learning Ops Engineer

Ensign InfoSecurityFrom company site

Salary

Not stated by the company

When

Full-time hours

Where

Singapore

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Start: Not stated
Work mode: On-site
Experience: Experienced

About the role

Ensign is looking for a senior engineer to take charge of its in-house AIOps, ML and LLM platform, along with the cloud and on-premise Kubernetes setups that support it. You will work closely with client-facing, security, compliance and internal teams to turn their needs into workable platform designs, and you will keep production ML and LLM workflows running smoothly. This role suits an experienced platform or software engineer who is comfortable across Linux, Kubernetes and cloud environments and enjoys both building and troubleshooting.

What you'll do

Own the design, build, upkeep and ongoing improvement of the in-house AIOps, ML and LLM platform, plus related cloud and on-premise Kubernetes solutions

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Work with cross-functional teams to turn client, security, compliance and internal requirements into practical platform designs

Build and run production ML and LLM workflows, covering retraining, deployment, inference serving, monitoring, rollback and optimisation

Troubleshoot production problems spanning applications, infrastructure, networking, Linux, Kubernetes and ML serving layers

Good to know

Full-time role based in Singapore

Requirements

Strong software and platform engineering fundamentals, including system design, API design, distributed systems, scalability, reliability, observability, authentication and authorisation, testing, and maintainable code
Practical grasp of the ML and LLM lifecycle, including data pipelines, model training and retraining, evaluation, experiment tracking, deployment, monitoring and production feedback loops
Strong Python development experience, with working proficiency in Go and C++ for reading, debugging, maintaining and extending existing production codebases
Strong Linux, networking and Kubernetes fundamentals, including production troubleshooting, service connectivity, ingress, resource limits, workload debugging and deployment operations
Experience designing, deploying and operating production platforms on AWS, Azure, GCP or on-premise environments
Experience building CI/CD, automation and MLOps or LLMOps workflows for production ML and LLM systems

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Ensign InfoSecurity

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Senior Machine Learning Ops Engineer

About the role

What you'll do

Good to know

Requirements

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