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Build and deploy AI models using Python, PyTorch/TensorFlow, and frameworks like LangChain and LlamaIndex; package models for production and collaborate on R&D.
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.
Design and implement end-to-end AI solutions using Python, PySpark, Databricks, Postgres and open-source LLMs like Qwen, building data pipelines, RAG systems and AI agents for enterprise clients.
Build and maintain ETL pipelines, optimize MongoDB schemas, and create Metabase dashboards to power AI-driven travel rental analytics and reporting.
Build and maintain scalable Python-based backend services using FastAPI, Docker, and cloud platforms (Azure/AWS) for a cybersecurity-focused AI project.
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.
Build and maintain enterprise web apps for a large insurer/reinsurer using Angular (frontend) and Java/Jakarta EE (backend), with AI-assisted coding and RAG/LLM tooling to speed delivery.
Build LLM-based agents and RAG systems for autonomous network operations, integrating fault diagnosis, predictive analytics, and closed-loop decision support using Python, LangChain, and vector/graph databases.
Build and deploy enterprise Generative AI apps using LLMs, RAG pipelines, and AI agents with Python, LangChain, and vector databases.
Build and scale high-throughput Python backends (FastAPI/Django) and TypeScript/React frontends for global clients, focusing on async APIs, Celery tasks, and PostgreSQL.
Build and scale high-performance Python backends (FastAPI/Django) and TypeScript/React frontends for global clients, focusing on clean architecture, async systems, and end-to-end delivery.
Design and build a scalable data infrastructure for a global PE fund using Snowflake, dbt, and Azure Data Factory to support analytics and AI initiatives across investment teams.
Build and deploy AI-powered healthcare tools using LLMs, RAG, and multi-agent systems to improve senior care access and outcomes.
Build production-grade AI agents in Python using LangGraph/LangChain, orchestrating multi-step reasoning, RAG, and tool use; deploy on cloud stacks with LLMOps and security guardrails.
Build and deploy generative AI systems (LLMs, RAG, agentic workflows) for defense operations, integrating Azure OpenAI, LangChain, and vector databases to support U.S. COCOM missions.
Build and deploy enterprise LLM applications, RAG systems, and AI agents using open-source models (DeepSeek, Qwen, Kimi) and frameworks like LangChain and vLLM.
Build and scale an internal AI platform using LLMs, vector databases, and agent frameworks to automate engineering workflows and create AI assistants for sales, design, manufacturing, and leadership.
Job Description We are seeking experienced AI Engineers / Agentic AI Developers to design, develop, and deploy enterprise-grade AI solutions with a focus on Agentic AI , Large Language Models (LLMs) , and…
ML engineer builds and optimizes RAG pipelines, fine-tunes VLM for technical docs, and prepares on-prem LLM/VLM inference for an AI platform serving engineers.
Build and deploy generative AI solutions using RAG, prompt engineering, and frameworks like LangChain on Google Cloud or Azure.
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