AI Automation Engineer
The role involves designing and deploying AI agents and complex automation workflows using n8n. You will also integrate custom AI models and third-party APIs into applications using React and TypeScript.
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The role involves designing and deploying AI agents and complex automation workflows using n8n. You will also integrate custom AI models and third-party APIs into applications using React and TypeScript.
Design and implement AI-driven solutions, intelligent agents, and automated workflows to optimize operational processes. Integrate various platforms and APIs while generating actionable insights and intelligence for the organization.
The AI Platform Engineer will own the technical architecture, roadmap, and governance of the organization's AI platform. They will collaborate with cross-functional teams to build and deploy secure, scalable AI agents and workflow automations.
Build and deploy AI agents using frameworks like Azure AI Foundry and Copilot Studio to solve business problems. Manage the full lifecycle of agent development, including prompt engineering, orchestration, evaluation, and production monitoring.
Develop reusable APIs, connectors, MCP tools, and service contracts, and integrate AI agents with enterprise systems such as SharePoint, SAP, Databricks, and SQL. Implement workflow orchestration and operational controls, including state management, retries, exception handling, access restrictions, API governance, reliability, and scalability.
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Build and ship AI-powered products, scalable Python and FastAPI backend services, and data pipelines, taking features from initial idea through deployment and production. Make architectural decisions, evaluate practical uses for generative AI, and own the reliability, observability, and performance of the systems you build.
Build full-stack AI features on AWS and Azure, using Claude and other models, while working closely with business users in a delivery pod. Maintain quality through testing and evaluations, follow the Solution Architect’s design direction, and identify gaps in the design.
You will identify, design, and implement AI-driven solutions and automation workflows to improve internal business efficiency. This involves mapping processes, building prototypes, and integrating AI systems with existing enterprise infrastructure.
Own AI-powered product features end-to-end, from discovery and requirements definition to shipping and performance monitoring. Collaborate with clients and internal teams to identify pain points and build automated solutions using LLMs and other technical tools.
Build and maintain agentic systems that collect, process, and surface signals from social, on-chain, and ad data sources. Develop full-stack applications and data pipelines to provide actionable intelligence for marketing and client teams.
Build and deploy LLM-powered tools and internal integrations to automate workflows for Finance, Legal, and Operations teams. Collaborate with cross-functional departments to identify process inefficiencies and implement practical, scalable automation solutions.
You will design, build, and validate rigorous multilingual benchmarks for large language models within terminal environments. This involves creating realistic task environments, calibrating task difficulty, and performing quality assurance through a multi-layer review process.
Design, build, and validate rigorous multilingual benchmarks for large language models using terminal-based workflows. Perform quality assurance, calibrate task difficulty, and analyze execution logs to ensure benchmark integrity.
Design, build, and validate rigorous multilingual benchmark tasks for large language models within terminal environments. Perform quality assurance through human review and automated checks to ensure benchmark integrity and accuracy.
The AI Benchmark Engineer will design, build, and validate rigorous evaluation tasks for large language models in a multilingual environment. Responsibilities include creating realistic task environments, writing deterministic verifier scripts, and performing quality assurance through a multi-layer review process.
The Senior Data Engineer will design and build AWS-native data foundations, including knowledge graphs and semantic layers, to support enterprise AI applications. They will lead the development of retrieval substrates and data pipelines while setting engineering standards for the broader team.
The engineer will translate business requirements into practical AI and software solutions by building prototypes and automating workflows. They will integrate various software systems, databases, and APIs while maintaining clear documentation for project handovers.
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The AI Automation Engineer will design, build, and deploy intelligent AI systems and automated workflows to improve operational efficiency and care quality. They will collaborate with cross-functional teams to integrate production-grade AI solutions, including LLMs and RPA, into enterprise healthcare workflows.
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