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Build, deploy, and maintain AI/ML models including LLMs and Agentic AI systems using cloud platforms like Azure and GCP to solve business and scientific problems.
Build and deploy agentic AI systems for clients, designing multi-agent workflows, RAG pipelines, and tool-using LLMs with AWS Bedrock and LangChain, while owning end-to-end delivery from architecture to production.
Lead the architecture and development of large-scale RAG and NLP systems for vertical AI platforms, using PyTorch, vector databases, and probabilistic modeling to deliver predictive intelligence for high-stakes industries.
Design and productionize enterprise AI solutions using Snowflake Cortex AI, Python, SQL, and RAG architectures to automate decisions and build intelligent agents for Hydro One’s data platform.
Build and maintain AI-powered full-stack apps using LLMs, RAG, and vector databases, with React frontends, Python/Node.js backends, and cloud-native DevOps.
Principal Data Engineer builds and scales AI-native data infrastructure for LLM-powered security products, including RAG and agentic systems at Exabyte scale.
Mid DevOps Engineer builds and automates CI/CD pipelines and cloud infrastructure on Microsoft Azure using Terraform and Azure DevOps, ensuring reliable deployments for global clients.
Build and optimize ETL/ELT pipelines, data lakes, and warehouses in Python on AWS/Azure to feed AI models and reports, while ensuring data quality and security.
Build and lead enterprise-scale AI systems, including multi-agent and RAG solutions, while driving innovation and scaling prototypes into production for Thomson Reuters’ AI-first transformation.
Lead AI/ML initiatives to build predictive models and GenAI solutions that enhance AutoTrader.ca’s automotive marketplace platform.
Build and own the data pipelines, ML feature stores, and inference APIs that power renewable-energy analytics at scale, integrating forecasts into a SaaS platform for wind, solar, hydro, and storage assets.
Builds cloud-native Java applications on Kubernetes and GCP, integrating DevSecOps and MLOps pipelines for a data/AI-focused product company.
Designs and builds scalable Azure-based data pipelines and analytics solutions using Databricks, Data Factory, and Java/Python microservices.
Build and deploy ML and agentic AI models on Databricks and cloud to personalize banking experiences, improve decisions, and drive operational efficiency in a regulated environment.
Builds and deploys generative AI models on GCP Vertex AI, designs scalable data pipelines, and collaborates on end-to-end AI product development using Python and MLOps.
Build and scale AI/ML systems for banking, including LLM apps, RAG pipelines, and agentic workflows, while ensuring secure, high-quality data pipelines and MLOps automation.
Build and deploy ML models and pipelines using Python, TensorFlow/PyTorch, and MLOps tools; analyze data to drive business decisions.
Design data models and pipelines for AI workloads, implement MLOps automation, and deploy scalable batch/streaming systems to support agentic workflows.
Build and maintain ETL pipelines, analyze financial data with Python/SQL, and create dashboards in Power BI to support treasury and finance decisions.
Build and maintain CI/CD pipelines, infrastructure-as-code, and AI-assisted engineering workflows for a robotics data platform, improving reliability and developer productivity.
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