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Build and deploy LLM-powered tools, RAG systems, and agentic workflows, then run the Kubernetes-based AI infrastructure that serves them reliably across the company.
Build AI-powered tools to accelerate hardware design and testing workflows using Python, TypeScript, and vector databases.
Design and deploy AI-powered solutions using LLMs, RAG, and NLP to build scalable, secure applications and automate business processes.
Build and own a production multi-agent AI system that automates research, content, and business workflows using Python, LLM APIs, RAG, and cloud infrastructure.
Build and deploy production-grade AI agents and RAG systems to automate network troubleshooting and change management using Python, C#, and agent frameworks.
Build and deploy generative AI models (LLMs, multimodal) using Azure Databricks, Hugging Face, and LangChain for content personalization, automation, and semantic analysis.
Build and operate AI platforms, services, and DevOps pipelines for Stadler’s AI Center of Competence, focusing on secure, scalable GenAI deployments in rail mobility.
Build and deploy production-grade generative AI and unstructured-data pipelines, including RAG, embeddings, and LLM-powered APIs, using Python, FastAPI, and cloud MLOps tooling.
Lead a team building Python-based generative AI solutions, guiding architecture, cloud deployment, and GenAI frameworks while mentoring engineers and aligning tech strategy with business goals.
Build and own the user experience for AI-powered healthcare tools, turning complex AI capabilities into intuitive, secure interfaces using React/Next.js and collaborating with ML teams.
Build and deploy LLM-powered assistants, RAG apps, and AI agents on enterprise platforms like ChatGPT Enterprise and AWS, using Python, TypeScript, and cloud-native tools.
Build enterprise GenAI applications using LLMs, RAG, and agentic workflows with Python, LangGraph, and cloud tools in a hybrid role.
Build and maintain PineStone’s data pipelines and warehouse in Snowflake using dbt and Matillion to feed investment analytics, reporting, and AI-driven workflows.
Build and ship AI systems for property managers: RAG pipelines, multi-step agent workflows, and LLM integrations using TypeScript, Python, and vector databases.
Design and implement scalable data architectures for AI systems, build robust pipelines, and lead MLOps/LLMOps deployments using Python, Spark, and cloud platforms.
Build and maintain ETL pipelines, streaming data flows, and vector databases to power AI-driven marketing platforms using Databricks, Spark, and GCP.
Principal ML Engineer at Grab’s AI Platform team, building and scaling ML infrastructure for Southeast Asia’s superapp, including LLM training/serving, fraud detection, and search ranking.
Build and operate an internal AI platform for LLM and agentic applications, including retrieval, deployment, and monitoring, using Python, FastAPI, and cloud services.
Build and deploy AI-powered solutions on AWS for a fintech company, focusing on generative AI, agentic AI, and document intelligence using services like Bedrock, Lambda, and OpenSearch.
Build and operate an internal AI platform that automates developer workflows, deploys LLM tools, and integrates AWS Bedrock and agentic frameworks to accelerate quantum-computing tooling.
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