Senior Staff AI Engineer (Knowledge Graph)

Ams Osram; Kuala Lumpur; On-site; MYR 120,000 - 180,000; Full time

Job summary

Ams Osram Central Functions is seeking an experienced AI/ML leader to design and implement Generative AI solutions across the organization. You will build AI-powered applications, optimize LLM-based workflows, and collaborate with data engineers and software developers to turn business challenges into scalable AI solutions.

Required: 7+ years in ML/AI, deep knowledge of GenAI, LLMs, RAG, knowledge graphs, and cloud AI services. Strong communication skills and stakeholder management are essential.

Qualifications

• Master's degree in Computer Science, Math, Statistics, Data Science, AI or related field.
• 7+ years of hands-on experience in Data Science, ML, LLMs and AI.
• Strong expertise in Generative AI, including LLMs, Knowledge Graphs, AI Agents, prompt engineering, model evaluation, and responsible AI.

Responsibilities

• Build AI-powered applications and Copilot experiences using LLMs.
• Design and optimize prompt engineering and retrieval-augmented generation (RAG) workflows.
• Develop knowledge graphs and graph databases (e.g., Neo4j) for enterprise AI solutions.
• Deploy and monitor models in production environments with MLOps practices.
• Collaborate with stakeholders to translate business challenges into scalable AI solutions.

Job description

Central Functions support the board as well as the business in achieving its strategic objectives. These are, for example, Information Technology, Human Resources, Logistics, Compliance, Finance and many more.

Generative AI Solutions:

• Build AI-powered applications, AI agents, and Copilot experiences using Large Language Models (LLMs) and modern AI technologies.
• Design and optimize LLM-based solutions, including prompt engineering, tool/function calling, Retrieval-Augmented Generation (RAG), GraphRAG, and agentic workflows.
• Design, develop, and integrate knowledge graph and graph database solutions (e.g., Neo4j, Cosmos DB) to model complex business relationships, enhance contextual understanding, and improve AI reasoning capabilities.
• Evaluate, fine-tune, and improve AI systems through structured testing, quality measurement, safety guardrails, and hallucination reduction techniques.

Machine Learning Models:

• Design and implementation of machine learning models in different domains (e.g. SCM, Manufacturing, Product Management and Corporate Functions) following the CRISP- DM process.
• Train, fine-tune, and optimize deep learning models for various applications.
• Work with large-scale datasets to preprocess, clean, and transform data for model training.

Data Pipelines & ML Model Deployment:

• Design and build robust data pipelines for AI applications.
• Collaborate with data engineers to optimize data infrastructure and storage for efficient ML processing.
• Deploy and monitor models in production environments.

Communication & Collaboration:

• Storytelling of results to business stakeholder.
• Collaborate with business stakeholders, architects, data engineers, and software developers to transform business challenges into scalable AI solutions.

Stay updated:

• Stay updated with the latest trends and advancements in data science and AI.

• Master's degree in Computer Science, Math, Statistics, Data Science, Artificial Intelligence or a related field.
• Over 7 years of experience in implementing machine learning solutions using Python, R, or similar languages, with extensive knowledge of various ML algorithms.
• Strong expertise in Generative AI, including Large Language Models (LLMs), Knowledge Graphs, AI Agents, prompt engineering, model evaluation, and responsible AI.
• 7+ years of hands-on experience in Data Science, Machine Learning, LLMs and AI.
• Deep understanding of Retrieval-Augmented Generation (RAG) architectures, including embeddings, vector databases, semantic search, and retrieval optimization techniques.
• Knowledge of Knowledge Graphs, semantic data models, ontologies, graph databases (e.g., Neo4j), and their application in enterprise AI solutions.
• Experience with GraphRAG and hybrid retrieval approaches combining vector search and knowledge graphs to improve AI accuracy, explainability, and contextual understanding.
• Hands-on experience with cloud-based AI services such as Azure OpenAI, Azure AI Foundry, Azure AI Search, or comparable platforms.
• Knowledge of MLOps and LLMOps, including model deployment, monitoring, evaluation, version control, testing, and governance.
• Excellent communication and stakeholder management skills.
• Strong interpersonal and collaborative skills.