AI Engineer
You will build tools to evaluate agent behavior, analyze traces, and implement features for automatic self-improvement. Additionally, you will run experiments on Mastra primitives to optimize configuration and performance.
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You will build tools to evaluate agent behavior, analyze traces, and implement features for automatic self-improvement. Additionally, you will run experiments on Mastra primitives to optimize configuration and performance.
Architect and build backend services, data pipelines, and orchestration workflows for LLMs and agent-based systems. Collaborate with cross-functional teams to translate complex business requirements into scalable AI platform components.
The AI Engineer will identify manual or outdated business processes across the company and replace them with AI agents or automations. They are responsible for building, deploying, and measuring the impact of these tools while fostering AI adoption among colleagues.
You will design, develop, and deploy AI-powered features and backend services while collaborating with cross-functional teams to ensure high-quality code. The role involves building LLM agents, managing MCP servers, and contributing to technical architecture discussions.
You will design and develop AI systems in production while collaborating with data, engineering, and product teams. Responsibilities include defining evaluation metrics, monitoring model quality, and iterating on AI products to solve real customer business problems.
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You will build and integrate AI-powered features into production products while developing LLM-based applications using agents and RAG. Additionally, you will design AI pipelines and evaluate model performance to ensure high-quality, scalable solutions for millions of users.
You will be responsible for developing and maintaining scalable software solutions while integrating AI agents into production environments. The role involves working across the full development lifecycle, including architecture, deployment, and support for AI-native products.
Design, develop, and implement Generative AI and Agentic AI solutions using platforms like Azure AI Foundry and Amazon Bedrock. Integrate intelligent agents with corporate systems and APIs while optimizing performance and scalability.
The AI Engineer will design and operate agentic workflows for the ReaderSight platform, including LLM integration and automated scoring runs. They are responsible for building prescriptive engines, implementing LLM solutions, and managing automated report generation pipelines.
Design, build, and implement AI-powered solutions including Generative AI, LLMs, and intelligent automation to solve business challenges. Collaborate with cross-functional teams to integrate AI capabilities into existing platforms and ensure production-ready scalability.
Design, develop, and deploy multi-agent AI systems and RAG pipelines for reasoning and task execution. Collaborate with engineering teams to integrate data sources and implement observability frameworks for model performance monitoring.
The AI Engineer will build end-to-end product capabilities using advanced AI tools, covering everything from cloud infrastructure to user-facing interfaces. They are responsible for designing agentic systems, implementing security controls, and ensuring the reliability and quality of AI-driven software.
The AI Engineer will design, develop, and maintain production-grade AI services and APIs using Python and modern orchestration frameworks. They will also build and optimize end-to-end RAG solutions while ensuring system quality through observability, evaluation, and performance monitoring.
You will build agentic pipelines to automate the production of serial episodes from script to final edit. Additionally, you will transition prototypes into production-ready systems and design a unified knowledge base for human-agent collaboration.
You will design and build AI agents using Azure AI Foundry and integrate them with enterprise tools like Microsoft Fabric. Additionally, you will manage the full lifecycle of these agents, including development, testing, deployment, and monitoring to ensure business performance.
You will design and implement production-grade AI agents and multi-agent systems that leverage enterprise data and modern web platforms. Additionally, you will act as a hands-on delivery consultant to scope work, communicate technical trade-offs, and build robust AI applications.
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Design and implement production-grade AI agents and multi-agent systems that leverage enterprise data and modern web platforms. Act as a hands-on delivery consultant to build and manage AI tools, connectors, and retrieval-augmented generation pipelines.
The AI Engineer will build and deploy agent harnesses using Python, LangChain, and LangGraph to automate analyst workflows and surface insights. They will also own the reliability, evaluation, and production monitoring of these AI agents within the Amazon AgentCore platform.
You will design, deploy, and manage intelligent AI agents and optimize complex RAG pipelines to automate customer tasks. Additionally, you will collaborate with product teams to translate requirements into technical specifications while ensuring robust backend architecture and system health.
Design, develop, integrate, verify, and maintain AI applications and models based on customer and business requirements. Produce efficient, reusable software, integrate it with databases and security modules, document implementations, and contribute to code quality and process improvement.
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The AI Engineer will build and operate production-quality Python services, pipelines, and integrations that leverage LLMs and retrieval systems. They will also collaborate with cross-functional teams to ensure AI implementations comply with security standards like HIPAA and SOC 2 while maintaining operational quality.
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