AI Engineering Consultant (Based in Kuala Lumpur, Malaysia)

Abeam-Consulting-; Kuala Lumpur; On-site; MYR 180,000 - 280,000; Full time

Job summary

ABeam Consulting is seeking an experienced AI Engineer to design, develop, and deploy enterprise AI applications and agents in a fast-paced environment. You will work with business users to identify high-value AI use cases, translate processes into scalable AI designs, and implement robust back-end services using Python and FastAPI.

Strong experience with LLMs, RAG, and agent frameworks is required, along with designing multi-agent workflows and tool integrations for enterprise systems.

Qualifications

• Experience in AI engineering and agentic AI development.
• Hands-on work with LLMs, RAG, and AI solution design.
• Experience delivering AI from POC to production.

Responsibilities

• Identify, assess, and prioritize AI use cases with stakeholders.
• Design, develop, test, and deploy enterprise AI apps and agents.
• Architect multi-agent workflows and tool orchestration.
• Build AI solutions using LangChain, LangGraph, and related tooling.
• Integrate AI with enterprise data, APIs, and document stores.

Skills

AI Engineering

Generative AI

Agentic AI

Python

LLMs

APIs

Problem Solving

Stakeholder Communication

Education

Degree in Computer Science or related discipline

Tools

LangChain

LangGraph

Azure OpenAI

MCP (Model Context Protocol)

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.

Key Responsibilities

• Work with business users and stakeholders to identify, assess, and prioritize potential AI and Agentic AI use cases.
• Understand business processes, pain points, data availability, and system constraints, and translate them into appropriate AI solution designs.
• Design, develop, test, and deploy enterprise-grade AI applications and AI agents.
• Develop AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI frameworks.
• Design and implement single-agent and multi-agent workflows, including agent communication, task delegation, decision logic, memory, and tool orchestration.
• 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.
• Industrialize successful POCs into scalable and maintainable production solutions.
• Implement appropriate security, access control, guardrails, governance, observability, and monitoring for enterprise AI applications.
• Define evaluation frameworks and success metrics to assess AI agent performance, including response quality, accuracy, reliability, latency, token consumption, and business outcomes.
• 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

• Strong hands-on experience in AI Engineering, Generative AI, or Agentic AI development.
• Practical experience designing and implementing solutions using LLMs.
• Strong understanding of RAG architecture, including retrieval strategies, embeddings, vector search, hybrid search, reranking, and prompt engineering.
• Hands-on experience with LangChain, LangGraph, or equivalent agent orchestration frameworks.
• Experience designing multi-agent workflows and tool-calling/orchestration architectures.
• Strong programming skills in Python.
• Strong experience developing APIs, backend services, and integrations with enterprise applications.
• 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.
• Experience defining LLM/agent evaluation methodologies and performance metrics.
• Familiarity with cloud platforms and managed AI services, preferably Azure / Azure OpenAI.
• Experience and knowledge in Google Antigravity and Google Vertex AI are mandatory; experience with Microsoft Copilot, Copilot Studio, and Microsoft Foundry 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 deployment architectures.
• Experience working in consulting, banking, financial services, or other highly regulated enterprise environments.

Other Requirements

• Ability to work well within a multi-disciplinary team structure, but also independently.
• Strong analytical and problem-solving skills across both rule-based and AI-driven scenarios.
• Meet agreed deadlines, with demonstrable Productivity.
• Strong interpersonal, verbal and written communication skills.
• Ability to work in culturally diverse and inclusive environments.
• Flexibility in scheduling with willingness to work extra non-standard hours if required.
• Initiate, proactive and willingness to self-learn existing and new technology.
• Willing and able to travel 50% or more (domestic, regional and international).

Education Requirements

• Degree in in Computer Science or related discipline.
• Written and spoken fluency in English with business proficiency in Mandarin and/or Bahasa mandatory.

Why Join Us

At ABeam Consulting, we place a strong emphasis on collaboration, and helping our employees grow and develop their skills, offering a supportive and empowering work environment. With a presence in multiple countries and a diverse range of clients, ABeam Consulting offers an exciting and dynamic workplace for individuals looking to build a career in consulting. ABeam Consulting has also recently joined SAP's regional strategic partner initiative as their first regional partner in the region and has also been recognized by UiPath as a Diamond Partner in providing RPA solutions. With such accolades, we aim to continue driving enterprise and digital transformation initiatives in order to transform the way people work and communicate in the digital age. In addition, our industry team is working tirelessly in order to bring more solutions to the banking and finance sector. We regret only shortlisted candidates will be notified.