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Education and Work Experience Requirements: · 5 to 8 years of experience as Data Scientist· 2 to 3 years of experience in Generative AI solution development· Strong understanding of AI agent collaboration, negotiation,…
Lead a team to build and deploy large language model applications, fine-tune models, and design RAG systems for mission-critical government use cases.
Own the enterprise search roadmap for Simpplr’s AI assistant (Magnus) and platform, driving relevance, retrieval, and RAG-powered answers while collaborating with AI and search engineers.
Senior Data Engineer builds and scales Python-based data pipelines (Pandas, PySpark, dbt, Airflow) on Snowflake/BigQuery, ensuring reliability and performance for analytics and AI workloads.
Build and deploy AI-powered healthcare solutions using LLMs, RAG, and Azure services to automate document processing and enhance clinical insights for payors and providers.
Build AI-powered multi-agent systems and full-stack applications using Node.js, React, and TypeScript, integrating LLMs, vector databases, and observability tools like Langfuse for agentic workflows and dashboards.
Build and deploy production-grade generative AI applications using Python, LLMs, and libraries like Transformers, LangChain, and Hugging Face.
Build and deploy GenAI and agentic AI systems for pharma clients using Microsoft Copilot Studio, ChatGPT Enterprise, and AWS Bedrock, integrating structured and unstructured data pipelines.
Build and deploy production-ready AI and multi-agent systems for healthcare, focusing on secure, compliant generative AI workflows using LLMs, RAG pipelines, and cloud platforms.
Build and scale Visa’s Java-based payment systems, integrating GenAI tools and LLM APIs to enhance backend services and developer workflows.
Leads a tiger team to modernize AI/ML platforms, integrating LLMs (RAG, embeddings) and automating data pipelines for customer identity resolution, while mentoring teams on next-gen AI development and .NET/React-based infrastructure.
Lead enterprise GenAI strategy, architect scalable AI systems, and advise C-suite on tech stacks, trade-offs, and ROI for large client engagements.
Designs and builds enterprise-grade Agentic AI and automation solutions using LLMs, CrewAI, and UiPath to automate workflows across Pearson’s education products.
Lead a team building AI-powered cloud systems at JPMorganChase, integrating LLMs, RAG pipelines, and agentic workflows while leveraging AI coding assistants daily.
Build and deploy LLM-powered applications for federal clients, implementing RAG pipelines, prompt engineering, and production AI infrastructure using Python, FastAPI, and cloud services.
Build and optimize production-grade AI systems using RAG pipelines, vector stores, and cloud-native tools to deliver secure, scalable solutions for federal environments.
Builds custom AI applications, RAG pipelines, and integrations for a law firm, translating legal workflows into production-grade AI solutions using Python, LangChain, and Azure.
Build and maintain Azure + Snowflake data pipelines using Python, SQL, and Azure Data Factory, while collaborating with AI coding agents in a modern, AI-native engineering workflow.
About the Role We are looking for a Fullstack Developer to design, build, and maintain web applications integrating modern Agentic AI capabilities for an client project/department. The ideal candidate is proficient…
Build full-stack web apps that embed generative AI features—modern UIs, conversational agents, and LLM APIs—while shipping secure, scalable backends on Azure or Google Cloud.
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