Permanent AI Engineer at Ang Mo Kio, Up to $6,000 (Office hours)

SUCCESS RESOURCE CENTRE PTE. LTD.; Singapore; In loco; SGD 61.000 - 73.000; Tempo pieno

Descrizione del lavoro

SUCCESS RESOURCE CENTRE PTE. LTD. in Singapore seeks an AI Engineer to lead R&D in agentic and multimodal AI, blending hands-on engineering with proposal development to turn ideas into prototypes.

You will design AI agents, integrate with enterprise systems, and help secure R&D funding while advancing capabilities in emerging AI technologies. This is a permanent, Mon-Fri role based in Ang Mo Kio.

Competenze

• Bachelor's degree in Computer Science, Computer Engineering, Electrical and Electronic Engineering, Data Science or a related discipline; a Master's degree or PhD in AI or machine learning is an advantage.
• At least 2 years of hands-on experience developing machine learning or deep learning solutions, including work with large language models or multimodal models.
• Experience writing technical proposals, research plans, grant applications or technical reports.
• Strong programming skills in Python, and proficiency with PyTorch and the Hugging Face ecosystem.
• Hands-on experience with LLMs and agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel) and with RAG, vector databases and prompt engineering.
• Experience with multimodal and vision-language models (e.g., CLIP, LLaVA, Qwen-VL or similar), and with speech, image or video processing.
• Familiarity with fine-tuning and optimisation techniques such as LoRA, quantisation and efficient inference (e.g., vLLM, TensorRT-LLM, ONNX Runtime).
• Working knowledge of Docker, Kubernetes, Git and CI/CD tools (e.g., GitLab CI/CD), and MLOps tools such as MLflow or Dataiku.
• Experience with evaluation methods for LLM and agent systems, including benchmarks, test sets and LLM-as-judge approaches.

Mansioni

• Identify R&D opportunities in agentic and multimodal AI that address business and customer needs. Develop R&D proposals covering technical approach, milestones, effort, budget and success criteria.
• Prepare funding submissions, present proposals to management, and build R&D partnerships with universities and research institutes.
• Design and build AI agents and multi-agent systems that plan, use tools and complete multi-step tasks. Integrate agents with enterprise systems and data using approaches such as function calling, MCP and RAG.
• Build guardrails and human-in-the-loop controls, and evaluate agent reliability, cost and latency.
• Develop and fine-tune multimodal models that combine text, images, video, audio and sensor data. Apply vision-language and speech models to tasks such as video understanding, document understanding and cross-modal search.
• Optimise open-weight models for deployment on-premise, in private cloud or at the edge.
• Build prototypes and proofs of concept, and evaluate them through well-designed experiments and benchmarks. Work with engineering teams to transition successful prototypes into products.
• Track advances in AI and share technology insights, reports and publications with management and colleagues. Apply responsible AI principles and comply with internal IT, cybersecurity and AI governance policies.

Conoscenze

Python programming

PyTorch

Hugging Face ecosystem

LLMs

Agent frameworks

RAG

Vector databases

Prompt engineering

Multimodal models

Speech/image/video processing

LoRA/quantisation

Docker

Kubernetes

Git

CI/CD

MLflow/Dataiku

Formazione

Bachelor's degree in CS/CE/EEE/Data Science or related

Master's or PhD in AI/ML (advantage)

Strumenti

Docker

Kubernetes

Git

CI/CD

MLflow

Dataiku

Descrizione del lavoro

• Permanent
• Up to $6,000
• Mon to Fri, 8am - 5.30pm
• Ang Mo Kio

About the Role

The AI Engineer drives the organisation's research and development in agentic AI and multimodal AI. The role combines hands-on engineering with research and proposal development: identifying promising AI opportunities, shaping them into well-scoped R&D proposals, and turning approved ideas into working prototypes and proofs of concept. The AI Engineer designs AI agents that can plan, use tools and complete multi-step tasks, and builds models and pipelines that understand and combine text, images, video, audio and other data. The AI Engineer helps secure R&D funding and builds the organisation's capabilities in emerging AI technologies.

Job Responsibilities

R&D Proposal Development

Identify R&D opportunities in agentic and multimodal AI that address business and customer needs. Develop R&D proposals covering technical approach, milestones, effort, budget and success criteria. Prepare funding submissions, present proposals to management, and build R&D partnerships with universities and research institutes.

Agentic AI

Design and build AI agents and multi-agent systems that plan, use tools and complete multi-step tasks. Integrate agents with enterprise systems and data using approaches such as function calling, MCP and RAG. Build guardrails and human-in-the-loop controls, and evaluate agent reliability, cost and latency.

Multimodal AI

Develop and fine-tune multimodal models that combine text, images, video, audio and sensor data. Apply vision-language and speech models to tasks such as video understanding, document understanding and cross-modal search. Optimise open-weight models for deployment on-premise, in private cloud or at the edge.

Prototyping and Evaluation

Build prototypes and proofs of concept, and evaluate them through well-designed experiments and benchmarks. Work with engineering teams to transition successful prototypes into products.

Research, Responsible AI and Knowledge Sharing

Track advances in AI and share technology insights, reports and publications with management and colleagues. Apply responsible AI principles and comply with internal IT, cybersecurity and AI governance policies.

Job Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Electrical and Electronic Engineering, Data Science or a related discipline; a Master's degree or PhD in AI or machine learning is an advantage.
• At least 2 years of hands-on experience developing machine learning or deep learning solutions, including work with large language models or multimodal models.
• Experience writing technical proposals, research plans, grant applications or technical reports.
• Strong programming skills in Python, and proficiency with PyTorch and the Hugging Face ecosystem.
• Hands-on experience with LLMs and agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel) and with RAG, vector databases and prompt engineering.
• Experience with multimodal and vision-language models (e.g., CLIP, LLaVA, Qwen-VL or similar), and with speech, image or video processing.
• Familiarity with fine-tuning and optimisation techniques such as LoRA, quantisation and efficient inference (e.g., vLLM, TensorRT-LLM, ONNX Runtime).
• Working knowledge of Docker, Kubernetes, Git and CI/CD tools (e.g., GitLab CI/CD), and MLOps tools such as MLflow or Dataiku.
• Experience with evaluation methods for LLM and agent systems, including benchmarks, test sets and LLM-as-judge approaches.

Success Resource Centre Pte Ltd (EA License Number: 04C3201 )
160 Robinson Road, #13-07/08/09 SBF Center, Singapore 068914
T: 6337 3183 | W: www.successhrc.com.sg