AI - ML Engineer
- Build AI-assisted features using approved model providers, orchestration frameworks and internal platform patterns.
30+ days
1,300+ Machine Technician Jobs in Malaysia | Job Vacancies | October 2026 | Maukerja - Page 45
Showing 1,360 jobs results for
"machine technician"
- Support the deployment of machine learning models into real-time and batch risk decision systems.
30+ days
- Build end-to-end solutions:Translate business and data challenges into scalable, secure, observable technical architectures. Communicate trade-offs clearly and produce practical designs that can be implemented by client and delivery teams.
30+ days
Sr. AI-ML Engineer
- Design and implement monitoring, alerting, and observability solutions for ML and AI systems, including model performance, data drift, latency, and AI operational health.
30+ days
Senior AI Engineer
- Model & Prompt Engineering: Design, test, and iterate prompts that improve reasoning, factuality, and user experience.
30+ days
- AI Solution Design & Development: Architect and build specialized AI systems tailored to client requirements, including custom NLP/LLM solutions, computer vision or data science pipelines, and retrieval-augmented generation (RAG) workflows for knowledge-intensive applications.
30+ days
Machine Learning Engineer
- Develop, deploy, and optimize machine learning and AI solutions that address complex business challenges.
30+ days
Machine Learning Engineer
- Develop, deploy, and optimize machine learning and AI solutions that address complex business challenges.
30+ days
Data Acquisition Engineer (AI Systems)
- As a Data Acquisition Engineer (AI Systems), you will work at the intersection of software engineering, browser automation, distributed systems, and AI. You will help design and build platforms that collect, process, and deliver structured data from a wide range of public sources while continuously improving their reliability and resilience.
30+ days
- Design and build data architectures specifically optimised for AI agent consumption: structured data stores accessible via tool-calling APIs, vector knowledge bases for semantic retrieval, graph databases for relationship-based reasoning, and hybrid retrieval systems that combine keyword, semantic, and structured query approaches.
30+ days
- Build AI-powered workflows, assistants, agents and automation systems.
30+ days
- Conduct literature reviews on AI benchmarking methodologies, evaluation metrics, and healthcare AI validation frameworks
30+ days
AI Engineering Graduate Talent Trainee
- Transforming Hyperscale Power into Useful AI: We take the extreme complexity of gigawatt-scale energy grids, liquid-cooled data center topologies, and low-latency networking fabrics, and turn that raw physical power into accessible, high-performance AI Cloud that directly drives real-world intelligence.
30+ days
AI Engineering Graduate Talent Trainee
- Transforming Hyperscale Power into Useful AI: We take the extreme complexity of gigawatt-scale energy grids, liquid-cooled data center topologies, and low-latency networking fabrics, and turn that raw physical power into accessible, high-performance AI Cloud that directly drives real-world intelligence.
30+ days
- Collaborate with developers to build and deploy AI use cases on Azure and AWS platforms
30+ days
- Educational Background & Technical Foundations Pursuing a degree in Computer Science, Data Science, Engineering, or a related field Strong fundamentals in programming (preferably Python) and SQL Basic understanding of data structures, algorithms, and software engineering principles 2. Data & AI Knowledge Familiarity with data processing concepts (ETL/ELT, data modeling, data cleaning) Exposure to machine learning fundamentals (e.g., supervised/unsupervised learning, model evaluation) Experience with common data/ML libraries (e.g., Pandas, NumPy, scikit-learn) is a plus 3. Tools & Platform Exposure Exposure to cloud platforms (e.g., AWS, Azure, or GCP) and modern data ecosystems is advantageous Familiarity with databases (SQL/NoSQL) and data warehousing concepts (e.g., Snowflake, BigQuery, Databricks) 4. Problem-Solving & Mindset Strong analytical thinking and problem-solving skills Ability to break down complex problems into structured solutions Eagerness to learn and adapt in a fast-paced, technology-driven environment 5. Communication & Collaboration Good communication skills, with the ability to explain technical concepts clearly Team player with a proactive and ownership-driven attitude Ability to work with cross-functional teams (analytics, business stakeholders)
30+ days
- Educational Background & Technical Foundations Pursuing a degree in Computer Science, Data Science, Engineering, or a related field Strong fundamentals in programming (preferably Python) and SQL Basic understanding of data structures, algorithms, and software engineering principles 2. Data & AI Knowledge Familiarity with data processing concepts (ETL/ELT, data modeling, data cleaning) Exposure to machine learning fundamentals (e.g., supervised/unsupervised learning, model evaluation) Experience with common data/ML libraries (e.g., Pandas, NumPy, scikit-learn) is a plus 3. Tools & Platform Exposure Exposure to cloud platforms (e.g., AWS, Azure, or GCP) and modern data ecosystems is advantageous Familiarity with databases (SQL/NoSQL) and data warehousing concepts (e.g., Snowflake, BigQuery, Databricks) 4. Problem-Solving & Mindset Strong analytical thinking and problem-solving skills Ability to break down complex problems into structured solutions Eagerness to learn and adapt in a fast-paced, technology-driven environment 5. Communication & Collaboration Good communication skills, with the ability to explain technical concepts clearly Team player with a proactive and ownership-driven attitude Ability to work with cross-functional teams (analytics, business stakeholders)
30+ days
- Support data generation and data augmentation activities to facilitate image recognition model development
30+ days
- Collaborate with developers to build and deploy CI/CD pipelines to streamline the deployment and data ingestion process
30+ days
Associate AI Engineer - Data Scientist
- Develop, test and maintain data analytics, machine learning and AI-assisted automation solutions using tools such as Python, SQL, Power BI, Power Automate, Alteryx or similar technologies.
30+ days
Agentic AI Engineer - Specialist
- AI agents & autonomous workflows
30+ days
- Optimize LLM inference performance on TPU/GPU and other AI accelerators
30+ days
AI Software Engineer
- Lead the development of advanced analytics, machine learning, and AI-driven solutions to address business challenges and generate actionable insights
30+ days
AI Engineer - Hybrid - Work from Home
- Develop and integrate AI-powered features into our software products.
30+ days
- Build AI agents, workflows, tools and automations that solve real customer, product and operations problems.
30+ days
- Build AI agents, workflows, tools and automations that solve real customer, product and operations problems.
30+ days
Research Engineer- Fellow (Applied AI research in computer vision, vision-language models) (XBJ)
- Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met.
30+ days
- Engage in forward-looking exploration and research of foundational AI technologies, including but not limited to large model pre-training and post-training paradigm innovation exploration, architecture design for multimodal understanding and generative models, next-generation foundational architectures (e.g., Transformer-next), and acceleration of large-scale model training and inference.
30+ days
Principal MLOps Engineer
- National payments infrastructure with real economic and systemic impact
30+ days
- Design, build, and deploy core components of our ML infrastructure platform on Kubernetes.
30+ days