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Build cloud-native services and AI-driven data pipelines for manufacturers, turning operations data into actionable insights using TypeScript/Python/Go, Kubernetes, and cloud platforms.
Build and deploy production-grade agentic AI systems using Python/Java and React, orchestrating multi-agent workflows and retrieval pipelines while ensuring reliability and observability.
Build scalable data pipelines and infrastructure for AI systems using Python, SQL, Spark, and cloud platforms to power LLMs, retrieval systems, and agentic architectures.
Lead a team building an AI-powered document processing platform for a global bank, using Python, React, and Google Cloud to automate workflows and integrate LLMs.
Build LLM-powered automations, chat/voice assistants, and RAG pipelines using Python, FastAPI, and vector databases; deploy cloud-native services with CI/CD and guardrails.
Build and deploy AI/ML systems for banking, including LLM pipelines, real-time microservices, and MLOps tooling using Python, cloud platforms, and frameworks like LangChain.
Build and deploy AI/ML systems for banking, including LLM pipelines, real-time model serving, and MLOps tooling in Python and cloud platforms.
Build and ship production-grade AI agent systems end-to-end, designing architectures, writing code, and implementing rigorous evals and observability for reliable agentic products.
Build and harden AI-powered healthcare workflows (LLM agents, voice systems) for pharmacists, owning end-to-end delivery, reliability, and customer feedback loops in a TypeScript/Node stack.
Build and deploy AI/ML systems for banking, including LLM pipelines, real-time APIs, and MLOps tooling in Python and cloud platforms.
Build and deploy AI/ML pipelines, fine-tune LLMs, and integrate generative AI into banking systems using Python, cloud platforms, and MLOps tools.
Build and scale AI/ML pipelines and GenAI systems for GE HealthCare, automating model deployment, monitoring, and lifecycle management across hybrid/multi-cloud (AWS, Azure).
Build and scale Python-based backend services and LLM agent frameworks, integrating speech/NLP tech and ensuring reliability with observability tools.
Lead AI Engineer builds and deploys generative AI and agentic systems for financial services, focusing on multi-modal LLMs, scalable MLOps pipelines, and enterprise integration.
Build and deploy production LLM applications for a global online gaming group, focusing on customer support automation, safer gambling, compliance, personalization, and internal tooling.
Build and lead a multi-agent LLM system using LangGraph to automate complex workflows for enterprise clients in marketing and e-commerce.
Build and maintain AI agentic backend systems using Python, LangChain/LangGraph, and vector databases to power autonomous workflows and RAG pipelines for business intelligence.
Lead DevOps Engineer builds and maintains scalable, secure cloud infrastructure for AI-powered systems, focusing on reliability, observability, and performance under production load.
Principal Data Engineer builds and leads the AI data stack for Anaplan’s LLM and agentic systems, designing retrieval layers, vector/graph databases, and real-time GenAI features for enterprise planning workflows.
Principal Data Engineer builds and leads AI systems at Anaplan, designing retrieval layers, RAG pipelines, and GenAI features that integrate LLMs into real-time planning workflows.
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