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About AI71: AI71 is an industry leader in artificial intelligence, delivering innovative solutions that empower developers, businesses and governments to solve complex challenges. AI71 builds secure, enterprise-ready…
ML Engineer develops AI agents and ML solutions for Sberbank’s B2B products, focusing on Python-based agent orchestration, model integration, and deployment into production pipelines.
Owns financial data migration for Oracle Fusion while building AI-powered agents and LLMs to automate data quality, anomaly detection, and predictive analytics in finance operations.
Agentic AI Engineer building autonomous, goal-oriented AI systems using agent orchestration frameworks, RAG, knowledge graphs, and fine-tuning Small Language Models for edge deployment.
The Data Scientist will develop AI and machine learning models, including Generative AI solutions, to support Singapore government agencies. The role involves full-stack development, data wrangling, and maintaining AI-powered applications using technologies like Python, AWS, and various LLM frameworks.
Design and build the AI layer for PebbleRoad's document intelligence and workflow products—selecting models, engineering prompts, evaluating output quality, and exposing capabilities via APIs—using AWS, LLMs, OCR, RAG, and vector databases.
The AI Engineer will design, build, and deploy enterprise-grade Generative AI applications, including RAG solutions and agentic workflows, to support digital transformation. The role involves hands-on development with LLMs, Python, and cloud AI platforms while ensuring system performance, safety, and governance.
This role involves designing and developing production-ready AI applications and agentic systems using LLMs, RAG, and workflow orchestration. Engineers will build scalable backend services and optimize AI models for performance and reliability in enterprise environments.
Hands-on data engineer building production data pipelines and agentic AI components (RAG, tool-calling agents) for clinical and non-clinical data at Lilly, using Python, Spark/PySpark, Databricks, and LLM frameworks in a regulated pharma environment.
The Junior AI Engineer will build and maintain LLM-powered applications, including RAG pipelines and chatbots, while integrating AI features into existing logistics systems. The role involves fine-tuning open-weight models and developing front-end interfaces using React and Next.js.
Develops and optimizes large-scale AI/GenAI models, including LLM training, GPU infrastructure, and agentic AI systems for enterprise use.
Builds AI-native engineering software for X-energy’s Xe-100 nuclear reactor project, focusing on agentic workflows, data integration, and cloud-native platforms to accelerate regulated workflows.
Builds AI-native engineering software for nuclear reactor development, focusing on agentic workflows, data integration, and cloud-native platforms to accelerate workflows and ensure regulatory compliance.
AI Platform Engineer at DXC Technology in Mexico City designing and building the AI foundation for a next-gen enterprise CPQ platform, using Python, LLMs (Claude/GPT), vector databases, and MCP to transform a legacy application into an AI-native solution.
Build and deploy generative AI features and full-stack apps to modernize Barclays’ platform, using Python/Java, cloud-native tools, and enterprise-grade security.
Builds AI-powered automation solutions for internal business processes (Finance, HR, Logistics) by designing agentic systems, integrating LLMs/RAG, and deploying scalable production-grade models while ensuring security, governance, and observability.
Architect, build, and scale backend services for an agentic AI platform at Adobe, designing APIs, data pipelines, and agent orchestration infrastructure using Python, LLMs, vector databases, and cloud platforms.
Builds and supports AI agents, copilots, RAG workflows, and automation solutions for Acosta Group, using GenAI/LLM frameworks (LangChain, LangGraph, CrewAI, etc.) and enterprise platforms like Azure AI and Microsoft Copilot Studio.
Leads enterprise-wide GenAI and agentic AI projects for Citi’s Controls Technology platform, architecting production-ready solutions using foundation models, RAG, knowledge graphs, and multi-agent orchestration to enhance operational efficiency and business value.
Builds and deploys enterprise-scale AI systems, focusing on multi-agent architectures with LLMs for healthcare workflows using Google Cloud tools.
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