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Build and maintain the AI platform and data lakehouse, automating operations and integrating services with APIs and cloud infrastructure using Python, Kubernetes, and MLOps tools.
Build and deploy AI-driven solutions like chatbots and ML models while collaborating with clients to translate business needs into practical generative AI applications.
Lead enterprise AI adoption by designing governance frameworks, productivity KPIs, and automated acceptance platforms to evaluate and scale AI solutions across finance and operations.
Builds and maintains full-stack web apps while integrating AI/Generative AI models to enhance user experiences and functionality.
Build and maintain full-stack web apps for finance clients, integrating AI models and cloud infrastructure with CI/CD pipelines.
Build and deploy AI-powered banking apps using LLMs, RAG pipelines, and Python; automate workflows with n8n and Kubernetes.
Design and deploy AI/ML models (LLMs, GenAI, CV) and agentic systems for clients, using Python, TensorFlow/PyTorch, and cloud platforms.
Designs and owns scalable AI architectures for enterprise clients, blending Azure cloud solutions, Power Platform low-code tools, and agentic AI frameworks like LangGraph and RAG systems.
Build and maintain automated test frameworks for AI agents and LLM-powered apps, including eval pipelines, chaos testing, and CI/CD integration across LangGraph and Vercel stacks.
Design and deploy multi-agent AI systems for logistics workflows, integrating LLMs with enterprise tools using frameworks like CrewAI and LangGraph.
Builds full-stack AI applications using GenAI models, cloud services, and modern web tech to create conversational interfaces, admin portals, and AI orchestration pipelines.
Build AI-powered integrations and agent-driven workflows using TypeScript and LLMs to automate data collection and orchestration across systems.
Build and deploy enterprise-grade autonomous AI agents using LLMs, orchestration frameworks, and vector databases in Azure to power resilient workflows and RAG systems.
Design and build generative AI workflows using Python, LangChain, and FastAPI, focusing on LLMs and RAG systems for healthcare-adjacent applications.
Builds and deploys AI-powered applications using LLMs, RAG, and agentic AI; integrates with cloud services and vector databases.
Design and govern end-to-end enterprise GenAI architectures on Databricks, integrating LLMs, vector search, and agentic workflows with Python and Azure.
Build next-gen AI-powered security tools by designing multi-agent systems in Python, integrating with security tools via MCP, and developing RAG pipelines for autonomous pentesting and remediation.
Leads quality engineering for an enterprise AI platform, building test frameworks for Agentic AI workflows and LLM-based systems using Python, Java, and modern CI/CD stacks.
Build production-ready enterprise AI apps using LLMs, RAG, and AI agents with Python, FastAPI, and cloud platforms to automate workflows and unlock business knowledge.
Designs and builds scalable cloud data pipelines, warehouses, and analytics platforms using Python, SQL, Spark, and cloud tools like BigQuery and Snowflake, with optional Gen AI integration.
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