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Build data pipelines, develop ML models, and deploy production solutions for retail forecasting, inventory, and ecommerce in an on-site role collaborating with cross-functional teams.
Hands-on Data Engineer building scalable data pipelines and ML models on Google Cloud (BigQuery, Vertex AI) to solve demand forecasting, inventory optimisation and marketing attribution problems for an Australian outdoor retail company — fully on-site in Sydney.
The Agentic DevOps Lead will lead Agentic DevOps initiatives, architecting and operationalizing a reusable DevOps framework for agentic applications across AWS, Azure, and GCP cloud environments, while leading cross-functional teams of engineers and DevOps specialists.
Product Manager leads end-to-end AI agents and LLM solutions from discovery to deployment, defining specs, prompts, tools, and success metrics while collaborating with engineering, data, and business teams.
Build and maintain an autonomous AI agent platform for hardware validation, designing LLM agent frameworks, RAG pipelines, and developer tooling to automate testing and knowledge management without human intervention.
Design and implement Agentic AI solutions, build data pipelines for RAG systems, and develop LLM-based applications using frameworks like LangGraph/LangChain for Thales Group's defense-sector R&D projects.
Principal role designing and engineering enterprise-grade data platforms, integrating Agentic AI, and enforcing Architecture as Code with CI/CD for scalable, governed data products in fintech.
Build responsive web and mobile applications using React/React Native while integrating AI/LLM capabilities—prompt engineering, AI agents, RAG—using Python and frameworks like LangChain.
Key Responsibilities: Design, develop, and deliver scalable microservices from architecture through production deployment. Lead backend system design and implementation, ensuring robust, secure, and maintainable…
Senior engineer builds autonomous AI agents and retrieval systems to modernize legacy software for federal healthcare, delivering explainable, traceable answers from decades of source code.
Designs and implements security controls for autonomous AI systems, RAG pipelines, and agentic workflows to prevent misuse, data leakage, and compliance violations in a regulated federal healthcare environment.
Senior AI engineer builds and optimizes agentic AI systems that analyze legacy software codebases for federal healthcare modernization, using LLMs, RAG, and autonomous agents to deliver explainable, traceable answers.
Designs and builds secure identity and access management systems for AI agents and enterprise applications, integrating with cloud IAM providers and enforcing Zero Trust principles in regulated environments.
Designs and builds proactive AI systems that reason, plan, and act autonomously for clients in sectors like biotech and robotics, using Python, TypeScript, and cloud-native tools.
Full Stack Engineer building enterprise applications infused with Generative and Agentic AI across the full SDLC — spanning React/Angular/Java/.NET/Node.js/Python stacks, cloud-native microservices, and LLM/RAG/agent integrations.
Build and deploy trustworthy, multi-agent AI systems that analyze sports performance data to provide calibrated, escalation-aware recommendations for coaches and athletes, using Python, Go, and AWS.
Embedded engineer at Google Cloud who codes, ships, and hardens production-grade agentic AI workflows (Gemini Code Assist, multi-agent systems) directly within customer environments using Google Cloud's Vertex AI stack and protocols like MCP/A2A.
This role involves architecting and building autonomous AI agents and RAG pipelines to automate enterprise workflows for non-technical users. The engineer will work on the full stack of LLM orchestration, tool-use, and production deployment using technologies like Python, Spark, and various vector databases.
Builds and deploys core AI platform components for NICE, including agent orchestration, RAG pipelines, and LLM integration, enabling enterprise automation across systems like Salesforce, ServiceNow, and Azure services.
The QA Lead will define and execute quality strategies for a cloud analytics platform, focusing on both traditional software and AI-powered, agentic systems. The role involves leading automation efforts, establishing testing methodologies, and ensuring reliability across CI/CD pipelines using Python, TypeScript, and various AI frameworks.
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