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Build and deploy AI systems using semantic search, RAG, and multi-agent workflows with LLMs and NLP/NLU techniques in Python and cloud platforms.
Build and deploy production-grade AI systems, including NLP, LLM features, and automations, to power workflows for restoration-industry SaaS customers.
Build and deploy production-grade AI/ML models using Python, TensorFlow, PyTorch, and MLOps tooling for scalable, real-world applications.
Build and ship LLM-powered features for enterprise clients, including RAG systems, AI agents, and vector-based retrieval pipelines using Python, LangChain, and cloud tools.
Build and deploy AI systems including LLMs, computer vision, and autonomous agents using Python, PyTorch, and LangChain, then productionize them with MLOps on cloud platforms.
Build and optimize GenAI systems using LLMs, prompt engineering, RAG, and agent workflows with frameworks like LangChain. Integrate APIs, vector databases, and external tools for scalable AI solutions.
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Build and deploy AI-powered insurance workflows using LLMs and agent-based automation to process submissions, policies, and client communications.
Build and deploy multi-agent AI systems using LLM APIs (Bedrock, OpenAI, Mistral), LangGraph/LangChain, and AWS serverless tools; focus on execution, not design.
Build and deploy LLM/NLP models using Hugging Face, LangChain, and cloud AI services; implement RAG pipelines and vector search for AI-driven chatbots and Q&A systems.
Build production-grade AI features like RAG pipelines and agentic workflows using Python, FastAPI, and vector stores; ship LLM-powered services end-to-end.
Design and deploy AI/ML models, including LLMs and GenAI, to solve healthcare data challenges using Python, cloud platforms, and MLOps.
Build and deploy production-grade ML models and RAG pipelines to replace rule-based systems, focusing on model quality, evaluation, and self-hosted LLM inference.
Build and deploy multi-agent AI systems using LLM APIs, agent frameworks, and AWS services, focusing on execution and integration rather than design.
Job Summary We are seeking an experienced AI Automation Engineering Manager to lead our AI and automation initiatives across the organization. This role is responsible for managing a team of AI Automation Engineers,…
Build and deploy AI-powered applications using LLMs, RAG pipelines, and vector search. Integrate LLM APIs into production systems with Python and cloud tools.
Build and optimize AI/ML models using Python, TensorFlow, and PyTorch, focusing on RAG, LangChain, and locally run AI with Ollama for production deployment.
Build and ship AI-powered features end-to-end: prototype LLM workflows in React/TypeScript and Python/Node, then take them to production across GuardPass’s security-training platform.
Build AI-powered systems using Python, FastAPI, and LLMs; develop agentic AI, RAG pipelines, and workflow automations; deploy on AWS.
Senior AI Engineer builds and operates a multi-agent GenAI system with retrieval, knowledge ingestion, and evaluation pipelines, integrating model APIs and cloud infrastructure.
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