Head, MLOPs, AI Platform, xCloud

HTX is the world’s first Science and Technology agency that integrates a diverse range of scientific and engineering capabilities to innovate and deliver transformative and operationally ready solutions for homeland security. As a statutory board of the Ministry of Home Affairs and integral to the Home Team, HTX works at the forefront of science and technology to empower Singapore’s frontline of security. Our shared mission is to amplify, augment and accelerate the Home Team’s advantage and secure Singapore as the safest place on planet earth. You will be part of the AI Platform Team — a group of passionate developers and engineers who believe that DevSecOps is a culture and that embracing AI is the future. The team is charged with transforming the way HTX delivers AI-infused software, leveraging the latest technologies to enable agile software development, zero-trust security, continuous DevSecOps, and artificial intelligence. As Head, MLOps, you will lead a team of engineers in designing, developing, and maintaining secure end-to-end DevSecOps solutions and processes. This is a hands-on technical leadership role sitting at the intersection of systems engineering, applied ML, and enterprise governance — pivotal in ensuring the security, scalability, and operational efficiency of software development and engineering workflows across HTX. You will lead the design, architecture, and delivery of HTX's enterprise-scale AI platform, spanning the inference stack that serves in-house and open-source LLMs, and the agentic AI platform that enables officers across Home Team Departments to build, govern, and automate their own workflows. Through platform engineering, you will enhance developer experience and productivity while safeguarding organisational assets. You will own the architectural direction of the AI platform, influence senior leadership on AI strategy, and build the team and partnerships needed to deliver at scale across on-prem, intranet, and air-gapped environments.

AI Platform Architecture & Delivery: Define and evolve the reference architecture for an enterprise agentic AI platform covering agent harnesses, sandboxing strategy, MCP connector gateways, durable execution, skills libraries, approval workflows, and structured audit trails. Shift the organisation from bespoke per-workflow builds to a platform where officers can describe what they need in natural language and have governed, versioned, repeatable workflows generated and executed. Inference Infrastructure: Own the roadmap and production operation of the LLM inference stack, covering vLLM/SGLang/TGI-class serving, GPU utilisation, quantisation, speculative decoding, KV-cache optimisation, and related throughput and latency work. Ensure the stack runs reliably across on-prem GPU clusters and air-gapped environments. Senior Leadership Engagement: Shape and communicate the AI platform strategy to chief executives and senior leadership across the Home Team. This includes building the business case, securing funding, and translating rapidly evolving technical trends (agentic harnesses, MCP adoption, managed agent services) into decisions the organisation can act on. Team Building & Mentorship: Grow the engineering team through hiring, onboarding, and structured internship programmes. Define reading lists, career paths, and technical standards. Mentor engineers across inference, platform, and agentic workflow areas.

● 8+ years of experience in ML platform, MLOps, or large-scale systems engineering roles, with demonstrated technical leadership delivering production AI/ML platforms ● Strong hands-on experience with LLM inference optimisation (vLLM, SGLang, TGI, TensorRT-LLM), including speculative decoding, quantisation, and KV-cache strategies ● Working knowledge of modern agent harnesses(Opencode, Hermes) and durable orchestration patterns, MCP and MCP gateways, and evaluation frameworks for agentic systems ● Understanding of sandboxing strategies across the isolation spectrum ● Strong expertise in container orchestration (Kubernetes, Helm), observability (Prometheus, Grafana), and MLOps tooling (MLflow or equivalents) ● Proficiency in Go, Python, Javascript ● Classical ML and data science fluency, with the judgement to know when a problem is better solved with causal inference, experimentation, or optimisation rather than a GenAI approach ● Experience building production ML systems with measurable business impact ● Enterprise architecture sensibility, with comfort designing for security-conscious, potentially air-gapped environments, and familiarity with identity services, document management, policy databases, and integration patterns across legacy enterprise systems ● Demonstrated ability to shape strategy with executives, build compelling narratives and pitches, and move funding and resource decisions forward ● Prior experience in government, defence, financial services, or other regulated environments ● Experience leading the shift from bespoke application delivery to platform-based operating models ● Platform thinking with a bias toward building the one platform that enables many workflows, rather than N bespoke applications ● Strong written and visual communication skills, capable of engaging both engineering teams and senior executives ● Collaborative approach working with data scientists, ML engineers, platform teams, and operational stakeholders across the Home Team ● Comfort with the operating-model shift implied by platforms, moving from engineers as builders to engineers as platform providers, and officers as workflow authors ● Proactive mindset focused on reliability, automation, governance, and sustainable delivery All new hires are appointed on a two-year contract in the first instance and will be assessed and considered for permanent tenure over time, based on performance. As part of the shortlisting process for this role, you may be required to complete a medical declaration and/or undergo further assessment. All applicants will be updated on the status of their applications within 4 weeks upon closing of the advertisement. #LI-HL1