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Design and build enterprise-grade AI systems using RAG, agentic workflows, and cloud platforms for HR and finance domains.
Lead a hybrid role managing data engineering teams and client projects, designing Azure-based cloud data platforms and GenAI-ready data architectures for banking, insurance, and retail clients.
Build and deploy generative AI agents, RAG systems, and ML models end-to-end using Python, LangChain, and Azure OpenAI to drive healthcare analytics and business impact.
Build and maintain data pipelines, clean and structure financial data, and integrate GenAI solutions like LLMs and RAG for a banking-focused project using Python, SQL, and ETL/ELT.
Builds AI agents and RAG systems to automate marketing workflows, integrate APIs via MCP, and deliver generative insights for ad-tech and media clients.
Build autonomous AI agents and RAG systems to automate marketing workflows, integrating LLMs, vector databases, and cloud platforms like GCP/AWS.
Build and deploy production-grade LLM-based applications using Python/Java/TypeScript, integrating with OpenAI, Claude, and open-source models while optimizing cost, latency, and security.
Build and maintain Python-based APIs and backend services for an AI company, using FastAPI/Django/Flask, async programming, and cloud tools like AWS/Azure.
Design and scale real-time data pipelines for AI systems, focusing on RAG, vector databases, and semantic layers to power agentic reasoning in enterprise environments.
Build real-time data pipelines and vector databases to power AI agents, transforming enterprise logs into embeddings for RAG systems with automated quality guardrails.
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 ship AI-powered web apps and agents using LLMs, Python/Node.js, and cloud tools; integrate AI into marketing platforms and internal tools for measurable business impact.
Build and scale production LLM-powered healthcare applications, including RAG pipelines, agentic systems, and evaluation frameworks, while ensuring compliance and reliability in a regulated environment.
Build and ship the AI backbone for a property-management assistant: RAG pipelines, multi-step agent workflows, and LLM integrations that power daily operations for 20,000+ HOA and condo communities.
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.
Build and optimize LLM-powered backend systems for scalable AI-driven search and agentic workflows, focusing on retrieval pipelines, orchestration, and evaluation.
Build and optimize AI-driven automation workflows using Python, n8n, and OpenAI models, integrating systems with PostgreSQL and APIs.
Build and optimize AI-driven automation workflows using Python, JavaScript, and tools like n8n or Zapier, integrating models and APIs for enterprise clients.
Build and run the production infrastructure for Ingersoll Rand’s GenAI program, automating CI/CD, observability, and reliability for LLM-powered apps on GCP and Snowflake.
Build and operate the cloud and DevOps infrastructure that powers Ingersoll Rand’s GenAI program, focusing on GCP, Snowflake, CI/CD, and observability for LLM-based applications.
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