HTX (Home Team Science & Technology Agency) Hiring! Full Time AI Engineer, LLM - LMM Deployment, Q Team CoE in - Ricebowl

What The Role Is

The Home Team Science and Technology Agency (HTX) is a statutory board under the Ministry of Home Affairs (MHA) which aims to pioneer innovation solutions and develop world class science and technology capabilities to transform and empower the Home Team in delivering safety and security for Singapore.

The HTxAI Products team is committed to empower our Home Team Department (HTD)'s flagship projects through the seamless integration of AI technologies. From identifying opportunities to delivery, launch, and continuous enhancement, HTxAI aims to push boundaries and setting new standards in public safety.

We're looking for an AI Engineer working on LLM/LMM Operations to join our team working at the intersection of large language models and production systems. You'll help build and maintain the systems that powers our model fine-tuning workflows, RAG pipelines and deployment processes while partnering closely with application teams.

You'll be joining a growing team where you'll have significant influence on our technical direction and practices as we scale our ML capabilities. We support continuous learning through conference attendance (such as NeurIPS, AAAI, WSDM) and relevant courses to help you stay current with the rapidly evolving ML landscape.

What You Will Be Working On

What You'll Do

Build and maintain RAG systems that reliably serve our applications

Establish fine-tuning pipelines for domain-specific models

Design evaluation frameworks and monitoring systems to track model performance in production

Deploy and optimize models in production environments

Collaborate with application teams to translate their needs into robust ML solutions

Build and maintain critical components of our ML systems from development through deployment

Our Infrastructure

The majority of our work runs on-prem where we maintain access to the latest GPUs in the market. This setup gives our team the freedom to experiment and iterate quickly on R&D projects without cloud cost constraints or API rate limits. You'll work directly with this hardware to improve training runs, benchmark different approaches, and push the boundaries of what's possible with current models.

We primarily work with open-source frameworks and in-house trained models, giving you the flexibility to choose the right tools for each challenge. While cloud experience is valuable and we do use cloud services for certain workloads, expect to spend most of your time working with our on-prem infrastructure.

What We Are Looking For

What We're Looking For

2+ years of hands-on experience with ML operations, ideally working with LLMs/LMMs or similar systems

Strong Python skills and experience with ML frameworks (PyTorch, Transformers)

Experience deploying models using Docker, Kubernetes, or cloud ML platforms

Practice building or maintaining production ML systems

Understanding of the operational challenges in keeping ML systems running reliably

Nice to Have

Experience with vector databases, embedding models, and retrieval systems

Familiarity with model evaluation methods and metrics

Experience with experiment tracking tools (MLflow, Weights & Biases, etc.)

Knowledge of CI/CD practices for ML workflows

You'll Thrive Here If

You care about the details but don't lose sight of the bigger picture

You can debug a failing deployment pipeline and think strategically about what metrics matter

You communicate well with both technical and non-technical teammates

You're comfortable working in an environment where requirements evolve as we learn

**** 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.