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Design and deploy AI/ML models (LLMs, GenAI, CV) and agentic systems for clients, using Python, TensorFlow/PyTorch, and cloud platforms.
Lead training, alignment, and optimization of large language models using RLHF, SFT, and quantization; build reward models, red-team models, and optimize inference pipelines in Python/C++/Rust.
Build and deploy deep-learning NLP models, fine-tune LLMs, and implement RAG and agentic AI systems while maintaining MLOps pipelines.
Build AI-powered data platform tools (React UIs, Python APIs, vector stores) that let users explore and query financial data in natural language while ensuring security and correctness in a regulated environment.
Build and deploy AI agents that automate financial workflows using Python, LLM APIs (Claude/OpenAI), and Microsoft ecosystems; focus on production-grade, secure, and scalable solutions.
Designs and deploys AI/ML models and GenAI solutions (LLMs, RAG) on Azure and Databricks, embedding intelligence into enterprise data pipelines and lakehouse architectures.
Design and deploy multi-agent AI systems for logistics workflows, integrating LLMs with enterprise tools using frameworks like CrewAI and LangGraph.
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 end-to-end agentic AI systems for industrial and water-treatment operations, focusing on reliability, safety, and continuous deployment.
Design and deploy enterprise AI systems, including multi-agent architectures and RAG pipelines, integrating LLMs with SAP and internal tools to drive logistics innovation.
Builds and deploys AI-powered applications using LLMs, RAG, and agentic AI; integrates with cloud services and vector databases.
Build and deploy AI agents and workflows that automate tasks, integrate LLM APIs, and ship production features end-to-end.
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.
Build AI agents and integrations that connect DevRev’s platform with customers’ tools using TypeScript, Python, and APIs; deploy serverless functions and optimize RAG pipelines for real-time workflow automation.
Build and deploy AI-driven applications and agents using Microsoft Copilot Studio, Azure OpenAI, and Azure AI Services to solve real business problems in a Microsoft enterprise environment.
Build and deploy AI models (LLMs, ML) for healthcare insights, recruiter automation, and personalized experiences using Python, PyTorch, LangChain, and vector databases.
Design and build cloud-native data pipelines and AI solutions using AWS and Snowflake, ensuring scalable, secure, and high-performance data infrastructure for analytics and AI initiatives.
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