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Build and maintain production data pipelines on Databricks, transforming raw data into clean, governed datasets for analytics and AI teams using PySpark, SQL, and Delta Lake.
Build, deploy, and own ML/AI models (including GenAI) for pricing, personalization, and fraud detection, using Python, PyTorch/TensorFlow, and MLOps tools in a cloud-native stack.
Builds, deploys, and optimizes AI/ML models and pipelines for enterprise products, integrating GenAI, NLP, and predictive analytics into scalable systems.
Build and deploy ML/AI models end-to-end, from data exploration to production, using Python, PyTorch/TensorFlow, and cloud platforms like AWS/GCP/Azure.
Build and deploy AI agents for supply-chain orchestration, integrating them securely with legacy systems using Python, GCP, and Vertex AI while optimizing token usage and performance.
Build and deploy AI agents for supply-chain optimization, integrating them securely with legacy systems using Python, GCP, and Vertex AI Agent SDK.
Designs and builds an AI-first platform for IP intelligence, focusing on modular, explainable systems for patent analysis and freedom-to-operate workflows using Azure, Databricks, and modern frontend stacks.
Principal engineer defining the ML platform architecture, standards, and MLOps practices for scalable AI/LLM training, deployment, and monitoring across PointClickCare.
Design and lead Databricks Lakehouse and Azure AI platforms, embedding GenAI, RAG, and AI agents to deliver scalable, governed data and AI solutions for enterprise clients.
Senior ML engineer builds NLP and computer-vision models to automate returns, disputes and call-center reviews for a large marketplace.
Build and deploy ML/LLM systems for personalization and teacher support in an edtech startup, owning the full lifecycle from modeling to deployment and monitoring.
Lead the design and delivery of an AI-native data platform, building LLM agents, semantic search, and anomaly detection from scratch while setting technical standards for the team.
Lead AI/ML and Agentic AI projects to build predictive, prescriptive, and generative models that drive revenue growth, demand forecasting, and business decisions using Python, Azure AI, and Databricks.
Designs and maintains SQL/ETL/BI pipelines to feed analytics and reporting for digital products and marketing, while leading AI/ML initiatives including model development, prompt engineering, and MLOps on Azure.
Design, build, and deploy enterprise AI/ML and LLM solutions using Databricks, MLflow, and cloud platforms, covering the full AI product lifecycle.
Lead AI/ML engineering projects, building and deploying LLM applications with RAG pipelines and MLOps tooling.
Build and deploy scalable AI/ML solutions for clients using Python, cloud platforms, and MLOps tools like Kubeflow and MLflow.
Design and lead enterprise-scale Data, AI, and Generative AI solutions for Malaysian enterprises and public-sector clients, including cloud-native architectures and MLOps/LLMOps pipelines.
Build and maintain MLOps pipelines to deploy, monitor, and scale AI/ML models in production using Azure, Python, and cloud-native tools.
Build and deploy AI/ML models, RAG pipelines, and Generative AI solutions for clients in banking and healthcare using Python, LLMs, and enterprise platforms like Azure ML and Databricks.
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