27 days ago
Singapore, SingaporeSenior
Responsibilities
- Design and build agentic AI systems, multi-agent frameworks, RAG applications, fine-tuned models, and other LLM-powered solutions.
- Develop classical machine learning and deep learning solutions for forecasting, classification, tabular modeling, and related use cases.
- Own end-to-end AI application development, including problem framing, data exploration, cleaning, integration, testing, deployment, and production operation.
- Build scalable backend services and APIs that expose AI capabilities to enterprise applications.
- Deploy and operate AI services in cloud, on-premises, Kubernetes, and fully air-gapped customer environments.
- Provision infrastructure, configure Kubernetes, Helm, ingress/TLS, and GPU enablement, and provide GPU sizing guidance based on serving performance.
- Build LLMOps infrastructure, monitoring, evaluation frameworks, guardrails, and human-in-the-loop checkpoints.
- Serve as a customer-facing technical contact, translating stakeholder needs into AI solutions and resolving support tickets.
- Run customer training, workshops, and enablement sessions.
- Contribute to pre-sales and proof-of-concept engagements through technical demonstrations and realistic engagement scoping.
- Plan, prioritize, and track concurrent customer engagements through completion.
- Create internal and customer-facing technical documentation, including runbooks, sizing notes, and post-incident write-ups.
Requirements
- At least 3 years of hands-on engineering experience, including at least 1 year working directly on agentic AI systems.
- End-to-end experience developing and deploying AI applications into production.
- Demonstrable experience building LLM-powered applications, including RAG pipelines, agentic workflows, or fine-tuned models.
- Experience deploying models and AI services in AWS, Azure, GCP, on-premises, or Kubernetes environments.
- Experience working in security-accredited or fully air-gapped environments, including offline delivery, image mirroring, private registries, and dependency bundling.
- Strong Python engineering skills and experience with PyTorch, TensorFlow, scikit-learn, LangChain, LlamaIndex, or equivalent tools.
- Ability to serve open-weight models with vLLM or equivalent across single- and multi-node deployments and tune serving parameters.
- Understanding of model-serving performance metrics including time-to-first-token, inter-token latency, throughput, concurrency, and context length.
- Working knowledge of AWS fundamentals, GPU cost optimization, Kubernetes, Helm, backend development, REST APIs, containerization, and CI/CD pipelines.
- Experience with LLM observability, including log aggregation, monitoring, and LLM-specific tracing.
- Ability to operate within change-control or accreditation processes and communicate complex technical concepts to nontechnical stakeholders.
- Preferred qualifications include regulated-industry AI deployment experience, customer-facing or forward-deployed engineering experience, enterprise hardening practices, and Kaggle or competitive ML experience.
Benefits
- Remote-friendly culture and flexible working environment.
- Market-leading total rewards, career growth, and membership in a world-class team.
- The position is based in Singapore and is associated with a hybrid work arrangement.
Tech Stack
AWSAzureDockerGoogle Cloud PlatformGrafanaHelmKubernetesPrometheusPythonPyTorchscikit-learnTensorFlow
Categories
Forward Deployed
