Lead Engineer/ Engineer, MLOps / SRE (Developer Experience), xCloud

What The Role Is

HTX is Singapore's Science and Technology agency that brings together diverse scientific and engineering capabilities to develop transformative, operationally ready solutions for public safety. As a statutory board under the Ministry of Home Affairs, HTX works at the forefront of science and technology to empower the Home Team with cutting‑edge capabilities. Guided by our mission to amplify, augment and accelerate the Home Team's advantage, we are committed to keeping Singapore the safest place on planet Earth.

xCloud is dedicated to elevating the enterprise experience through cutting‑edge cloud capabilities, including:

Sustainable enterprise data centres

Hybrid cloud platforms

Advanced cloud security measures

Integrated Development, Security, and Operations (DevSecOps)

Innovative enterprise Software-as-a-Service (SaaS) solutions

AI-powered enterprise applications

As the MLOps/SRE Engineer for HTX's developer experience squad, you will be responsible for deploying, operating, and optimizing a production LLM system in our secure infrastructure. You will ensure the agentic code assistant is reliable, performant, and cost‑effective, managing the full stack from LLM inference to vector databases, orchestration services, and observability. This role combines deep MLOps expertise with SRE discipline to support Home Team's critical AI infrastructure.

What You Will Be Working On

LLM Deployment: Deploy and manage LLM models using vLLM/TensorRT-LLM on our GPU infrastructure, optimizing for throughput, latency, and GPU utilization

Infrastructure Management: Provision and maintain the supporting infrastructure including vector databases (for RAG), orchestration services, Redis/queue systems, and API gateways

Performance Optimization: Profile and tune LLM inference performance, experiment with batching strategies, context caching, and quantization techniques to maximize throughput within GPU constraints

Observability: Implement comprehensive monitoring using Prometheus, Grafana, DCGM exporters, and Elastic Stack to track inference latency, token throughput, cache hit rates, and system health

Reliability Engineering: Establish SLOs/SLIs, implement auto‑scaling policies, design failure recovery mechanisms, and conduct chaos engineering to ensure high uptime

Cost Optimization: Monitor GPU utilization and inference costs, identify optimization opportunities, and implement strategies to reduce token usage and compute spend

Security & Compliance: Ensure all components operate within secure network boundaries, manage secrets and credentials securely, and maintain audit logs for compliance

Incident Response: Participate in on‑call rotation, troubleshoot production incidents, conduct root cause analysis, and implement preventive measures

Capacity Planning: Model future load, forecast GPU requirements, and work with infrastructure teams to scale the platform as adoption grows

What We Are Looking For

4+ years of experience in MLOps, SRE, or DevOps roles, with at least 1 year working with ML/AI systems

Hands‑on experience deploying and operating LLMs in production (vLLM, TGI, TensorRT‑LLM, or similar)

Strong Kubernetes expertise including operators, StatefulSets, and GPU scheduling

Deep understanding of GPU architecture, and inference optimization techniques

Experience with observability tools (Prometheus, Grafana, ELK/Elastic Stack)

Solid Python and Bash scripting skills for automation

Knowledge of vector databases (Milvus, Weaviate, Qdrant, or Pinecone)

Experience with infrastructure-as-code (Terraform, Helm, Kustomize)

Experience with NVIDIA GPUs (A100/H100/B200) and DCGM monitoring

Understanding of LLM inference concepts: KV cache, continuous batching, PagedAttention

Familiarity with Ray clusters, Kubeflow, or MLflow

Background in SRE practices: SLO/SLI definition, error budgets, incident management

Experience with secure or regulated environments

Knowledge of LiteLLM, Kong Gateway, or API management platforms

Systems thinking with ability to diagnose complex issues across the ML stack

Data‑driven decision making using metrics and telemetry

Proactive mindset focused on reliability, automation, and preventive measures

Strong debugging skills for GPU, networking, and distributed systems issues

Clear incident communication and documentation

Collaborative approach working with data scientists, ML engineers, and platform teams

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