AI Engineering / Consultant / Specialist (Based in Singapore)

ABEAM CONSULTING (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

ABeam Consulting (SINGAPORE) PTE. LTD. is hiring an AI Engineering Senior Consultant to identify, design, and deploy AI solutions across client environments in Singapore. You will develop AI agents, LLMS-based workflows, and robust backend services with Python, FastAPI, and RAG pipelines.

You will collaborate with business users and technical teams, ensuring security, governance, and reliable operation while delivering rapid POCs and production deployments.

Qualifications

• 2 years of relevant experience in software development, automation or AI.
• Basic understanding of LLMs, with practical exposure through coursework, projects, internships or work experience.
• Basic understanding of RAG and prompt engineering is an advantage.
• Exposure to LangChain, LangGraph or similar frameworks is an advantage.
• Experience designing multi-agent workflows and tool-calling/orchestration architectures.
• Practical Python programming skills, including the ability to write, debug and maintain scripts or applications.
• Basic experience working with APIs, JSON and databases. Academic or personal project experience is acceptable.
• Experience developing AI solutions from POC through production deployment.
• Experience integrating AI solutions with structured and unstructured enterprise data sources.
• Understanding of AI application architecture, security, governance, access controls, guardrails, observability, and monitoring.
• Ability to test AI outputs, identify issues and document results against defined criteria.
• Familiarity with cloud platforms and managed AI services, preferably Azure / Azure OpenAI.
• Experience and knowledge in Microsoft Copilot, Copilot Studio, and Microsoft are mandatory; experience with Google Antigravity and Google Vertex AI are good to have.
• Strong analytical and problem-solving capabilities.
• Ability to work independently and deliver solutions in a fast-paced environment.
• Strong communication, stakeholder management, and consulting skills.
• Ability to work directly with business users to convert business requirements into practical AI solutions.

Responsibilities

• Identify, assess, and prioritize potential AI and Agentic AI use cases.
• Understand business processes and requirements, and support the preparation of AI solution designs under the guidance of senior team members.
• Develop, test, and support the deployment of AI applications and AI agents based on agreed solution designs.
• Develop AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI frameworks.
• Develop AI agent workflows, including tool integration and workflow logic.
• Develop orchestration workflows using technologies such as LangChain, LangGraph, MCP, and similar agent frameworks.
• Integrate AI agents with enterprise applications, databases, APIs, document repositories, and external tools.
• Develop robust backend services and APIs using Python and frameworks such as FastAPI.
• Design and implement RAG pipelines covering document ingestion, chunking, embedding, retrieval, hybrid search, reranking, prompt construction, and response generation.
• Build reusable AI components, agent tools, APIs, connectors, and services to accelerate future AI implementations.
• Develop POCs rapidly and work with stakeholders to validate business feasibility, technical feasibility, and expected value.
• Support the enhancement, testing and deployment of successful POCs into production solutions.
• Follow established security and development standards, and support the implementation of access controls, guardrails, logging and monitoring.
• Execute test scenarios and evaluate AI outputs against agreed criteria, documenting issues and improvement opportunities.
• Establish feedback loops and continuously improve AI agent performance based on evaluation results and user feedback.
• Troubleshoot and optimize AI applications, including prompts, retrieval quality, agent workflows, model selection, and system performance.
• Collaborate with infrastructure, security, application, data, and architecture teams to support successful enterprise deployment.
• Communicate AI concepts, solution architecture, limitations, risks, and recommendations clearly to both technical and non-technical stakeholders.
• Prepare solution documentation, architecture designs, implementation plans, technical specifications, test scenarios, and operational documentation.
• Provide knowledge transfer and technical guidance to client teams to support sustainable adoption of AI solutions.

Skills

Python programming

APIs & JSON

Stakeholder management

Analytical thinking

Independent work

Consulting skills

Problem solving

Tools

Azure

Copilot Studio

Microsoft Copilot

Google Vertex AI

Job description

About Us

ABeam Consulting is a global professional services company that specializes in delivering business transformation and technology solutions to clients across a wide range of industries. With a global presence and over 9000 employees worldwide we aim to be the transformation partner of choice for all of our clients.

AI Engineering Senior Consultant

Location: Singapore

Key Responsibilities

• Work with business users and stakeholders to identify, assess, and prioritize potential AI and Agentic AI use cases.
• Understand business processes and requirements, and support the preparation of AI solution designs under the guidance of senior team members.
• Develop, test, and support the deployment of AI applications and AI agents based on agreed solution designs.
• Develop AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI frameworks.
• Develop AI agent workflows, including tool integration and workflow logic.
• Develop orchestration workflows using technologies such as LangChain, LangGraph, MCP, and similar agent frameworks.
• Integrate AI agents with enterprise applications, databases, APIs, document repositories, and external tools.
• Develop robust backend services and APIs using Python and frameworks such as FastAPI.
• Design and implement RAG pipelines covering document ingestion, chunking, embedding, retrieval, hybrid search, reranking, prompt construction, and response generation.
• Build reusable AI components, agent tools, APIs, connectors, and services to accelerate future AI implementations.
• Develop POCs rapidly and work with stakeholders to validate business feasibility, technical feasibility, and expected value.
• Support the enhancement, testing and deployment of successful POCs into production solutions.
• Follow established security and development standards, and support the implementation of access controls, guardrails, logging and monitoring.
• Execute test scenarios and evaluate AI outputs against agreed criteria, documenting issues and improvement opportunities.
• Establish feedback loops and continuously improve AI agent performance based on evaluation results and user feedback.
• Troubleshoot and optimize AI applications, including prompts, retrieval quality, agent workflows, model selection, and system performance.
• Collaborate with infrastructure, security, application, data, and architecture teams to support successful enterprise deployment.
• Communicate AI concepts, solution architecture, limitations, risks, and recommendations clearly to both technical and non-technical stakeholders.
• Prepare solution documentation, architecture designs, implementation plans, technical specifications, test scenarios, and operational documentation.
• Provide knowledge transfer and technical guidance to client teams to support sustainable adoption of AI solutions.

Key Requirements

• 2 years of relevant experience in software development, automation or AI.
• Basic understanding of LLMs, with practical exposure through coursework, projects, internships or work experience.
• Basic understanding of RAG and prompt engineering is an advantage.
• Exposure to LangChain, LangGraph or similar frameworks is an advantage.
• Experience designing multi-agent workflows and tool-calling/orchestration architectures.
• Practical Python programming skills, including the ability to write, debug and maintain scripts or applications.
• Basic experience working with APIs, JSON and databases. Academic or personal project experience is acceptable.
• Experience developing AI solutions from POC through production deployment.
• Experience integrating AI solutions with structured and unstructured enterprise data sources.
• Understanding of AI application architecture, security, governance, access controls, guardrails, observability, and monitoring.
• Ability to test AI outputs, identify issues and document results against defined criteria.
• Familiarity with cloud platforms and managed AI services, preferably Azure / Azure OpenAI.
• Experience and knowledge in Microsoft Copilot, Copilot Studio, and Microsoft are mandatory; experience with Google Antigravity and Google Vertex AI are good to have.
• Strong analytical and problem-solving capabilities.
• Ability to work independently and deliver solutions in a fast-paced environment.
• Strong communication, stakeholder management, and consulting skills.
• Ability to work directly with business users to convert business requirements into practical AI solutions.

Preferred / Added Advantage

• Experience with MCP (Model Context Protocol) and agent tool integration.
• Experience with self-hosted or open-source LLM deployment.
• Experience with LLM evaluation frameworks such as DeepEval or equivalent.
• Experience with model fine-tuning, including SFT or other post-training techniques.
• Experience working with enterprise document-processing, OCR, knowledge-management, or search solutions.
• Familiarity with Azure, AWS, containerization, and enterprise deployme