Senior AI Engineer (Contract)

ARGYLL SCOTT CONSULTING PTE. LTD.; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

ARGYLL SCOTT CONSULTING PTE. LTD. is seeking a Senior AI Engineer to design, develop and deploy AI-powered solutions in a small, autonomous team. The role focuses on end-to-end AI architectures, multi-agent systems, RAG and knowledge graphs to support public-sector style interactions.

The ideal candidate has 8+ years in AI, hands-on model fine-tuning, RLHF and experience delivering AI in production. A strong cloud and data ecosystem background is essential for integration and scalability.

Qualifications

• Advanced degree in Computer Science, Data Science, NLP, ML, DL or related field.
• 8+ years of professional experience designing, developing and deploying AI solutions.
• Hands-on experience fine-tuning and scaling generative AI models (transformers, RLHF).
• Proven experience deploying AI-powered products in production.
• Strong knowledge of RAG, knowledge graphs, prompt engineering and multi-agent architectures.
• Experience with MCP, AG-UI, A2A and A2UI.
• Familiarity with cloud platforms such as AWS, GCP or Azure.

Responsibilities

• Architect, develop and deploy end-to-end AI solutions, owning technical design and performance.
• Design scalable AI architectures including multi-agent systems, RAG, knowledge graphs and AI gateways.
• Develop and optimise generative AI models with fine-tuning and context management.
• Establish evaluation frameworks, guardrails and testing methodologies for AI models.
• Monitor production AI systems, addressing hallucinations, latency and reliability.
• Evaluate architectural approaches, tooling, performance, costs and scalability.
• Collaborate with product managers, data engineers, analysts and end users.
• Document evaluation methodologies and architectural decisions for iterative development.

Skills

LLMs

RAG

Multi-agent systems

Prompt engineering

Model fine-tuning

AI architecture

RLHF

Context engineering

Education

Advanced degree in Computer Science or Data Science

Tools

AWS

GCP

Azure

Job description

Senior AI Engineer (Contract)

Location: Singapore

Contract Type: 12 months contract (Extendable & Convertible)

Keywords: Generative AI, Large Language Models (LLMs), Agentic AI, Multi-Agent Systems, RAG, Knowledge Graphs, Model Fine-Tuning, AI Architecture, MCP, LLM Evaluation

Company/Role Overview

We are seeking a Senior AI Engineer to support the development of an AI-powered digital platform designed to simplify interactions between businesses and public-sector services. Working within a small, autonomous team, the role involves architecting, developing, deploying and optimising AI solutions that integrate information from multiple data sources and support intelligent, automated interactions. The position requires strong technical expertise in AI engineering, alongside the ability to make architectural decisions, evaluate model performance and deliver reliable AI capabilities that address real user needs.

Key Responsibilities

• Architect, develop and deploy end-to-end AI solutions, taking ownership of technical design, implementation, model evaluation and ongoing performance optimisation.
• Design and implement scalable AI architectures incorporating multi-agent systems, Retrieval-Augmented Generation (RAG), knowledge graphs, orchestration frameworks and AI gateways.
• Develop and optimise generative AI models, including fine-tuning approaches, retrieval strategies, prompt engineering and context management to improve model accuracy and reliability.
• Establish evaluation frameworks, testing methodologies and guardrails to assess AI model behaviour, identify limitations and improve the performance of agentic AI solutions.
• Monitor and continuously refine production AI systems, addressing challenges such as hallucinations, context degradation, prompt injection, latency and operational reliability.
• Evaluate architectural approaches, AI frameworks and tooling options, balancing model capabilities against performance, scalability, inference costs and operational requirements.
• Collaborate with product managers, data engineers, analysts and end users to understand requirements, evaluate practical outcomes and identify opportunities for improvement.
• Document AI evaluation methodologies, architectural decisions and technical limitations while supporting iterative development, frequent deployments and data-informed decision-making.

Essential Requirements

• Advanced degree in Computer Science, Data Science, Natural Language Processing (NLP), Machine Learning, Deep Learning or a related field specified above.
• Minimum 8 years of professional experience, with demonstrated expertise in designing, developing and deploying AI-driven solutions.
• Hands‑on experience fine‑tuning and scaling generative AI models, with a strong understanding of transformer‑based LLMs, Reinforcement Learning from Human Feedback (RLHF) and fine‑tuning at inference scale.
• Proven experience deploying and scaling AI‑powered products or solutions in production environments.
• Strong working knowledge of RAG, knowledge graphs, context engineering, prompt engineering, multi‑agent architectures and AI orchestration frameworks.
• Experience developing AI evaluation methodologies, implementing guardrails and assessing the reliability and performance of agentic AI solutions.
• Experience working with AI protocols such as Model Context Protocol (MCP), AG‑UI, Agent‑to‑Agent (A2A) and A2UI, alongside experience architecting scalable AI tooling ecosystems.
• Familiarity with cloud platforms and services, including AWS, Google Cloud Platform (GCP) or Microsoft Azure.

Additional Attributes

• Demonstrated ability to make independent technical and architectural decisions on AI‑driven products, including situations involving incomplete information or evolving requirements.
• Strong understanding of AI system performance, including accuracy, latency, inference costs, scalability and the trade‑offs involved in production environments.
• Ability to communicate complex AI concepts, technical decisions and system limitations clearly to both technical and non‑technical stakeholders.
• Product‑oriented mindset, with an emphasis on developing AI solutions that deliver measurable improvements in user experience and task completion.
• A portfolio demonstrating practical AI product development, including commercial implementations, open‑source contributions or independent projects.
• Interest in applying AI to improve digital services and simplify interactions between organisations and public‑sector systems.