Senior Machine Learning Ops Engineer
Ensign InfoSecurity Open · verified Oct 22, 2026 At a glance
Senior Machine Learning Engineering role at Ensign InfoSecurity. Singapore · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Design and evolve the internal AIOps, ML and LLM platform across cloud and on-premise Kubernetes environments
- Turn client, security, compliance and internal needs into platform designs with teams across functions
- Run production ML and LLM workflows, from retraining and deployment through monitoring, rollback and tuning
- Diagnose production faults across applications, infrastructure, networks, Linux, Kubernetes and model-serving layers
What you bring
- Strong Python development skills, plus Go and C++ ability to work on existing production code
- A practical grasp of the ML and LLM lifecycle, including training, evaluation, deployment and production feedback
- Solid Linux, networking and Kubernetes skills, including troubleshooting live services and workloads
- Experience operating production platforms in cloud or on-premise settings, and building CI/CD and MLOps workflows
Who this fits
This senior engineering role suits someone who can build and operate production ML and LLM platforms while working with AI, deployment, infrastructure and security teams. It calls for hands-on troubleshooting across software and infrastructure layers.
From the employer
Ensign is hiring !
Key Responsibilities
- Own the design, development, maintenance, and evolution of the in-house AIOps / ML / LLM platform, including related cloud and on-premise Kubernetes solutions.
- Translate client, security, compliance, and internal requirements into practical platform designs with cross-functional teams.
- Build and operate production ML / LLM workflows, including retraining, deployment, inference serving, monitoring, rollback, and optimisation.
- Troubleshoot production issues across application, infrastructure, networking, Linux, Kubernetes, and ML serving layers.
Qualifications / Requirements
- Strong software/platform engineering fundamentals, including system design, API design, distributed systems, scalability, reliability, observability, authentication/authorization, testing, and maintainable code design.
- Practical understanding of the ML / LLM lifecycle, including data pipelines, model training/retraining, evaluation, experiment tracking, deployment, monitoring, and production feedback loops.
- Strong development experience in Python, 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 / LLMOps workflows for production ML / LLM systems.
- Strong communication skills and ability to work with AI, deployment, infrastructure, and security teams.
Good to Have
- Deep experience operating Kubernetes in bare-metal, air-gapped, or restricted on-premise environments.
- Experience with MLflow, Kubeflow, vLLM, TensorRT, TGI, or similar ML / LLM platform tools.
- Exposure to TypeScript / React or Java-based services.