
1001 - 5000 employees
Founded 1996
đ€ B2B
B2B âą Data Analytics âą Consulting
Keyrus is an international company passionate about leveraging data to make impactful changes in life, society, and the future. With a presence in 18 countries, Keyrus is dedicated to creating meaningful careers for its employees by fostering excellence, trust, creativity, kindness, and fun. Specializing in data analytics, data advisory, and management, Keyrus provides vendor-agnostic solutions and continuous training opportunities through its KLX platform. The company also emphasizes a healthy work-life balance with a range of benefits, including sports events, healthcare plans, and an inclusive work culture. Keyrus is committed to openness and transparency to maintain positive workplace relationships and is driven to innovate and influence the digital future.
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1001 - 5000 employees
Founded 1996
đ€ B2B
B2B âą Data Analytics âą Consulting
Keyrus is an international company passionate about leveraging data to make impactful changes in life, society, and the future. With a presence in 18 countries, Keyrus is dedicated to creating meaningful careers for its employees by fostering excellence, trust, creativity, kindness, and fun. Specializing in data analytics, data advisory, and management, Keyrus provides vendor-agnostic solutions and continuous training opportunities through its KLX platform. The company also emphasizes a healthy work-life balance with a range of benefits, including sports events, healthcare plans, and an inclusive work culture. Keyrus is committed to openness and transparency to maintain positive workplace relationships and is driven to innovate and influence the digital future.
âą Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on delivery. âą Locate, qualify, and secure access to the data required for each use case, working directly with the Data Engineer. âą Translate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows. âą Prototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain experts. âą Work with Data Engineers, Software Engineers, Foundations Architects, Governance experts, Business Value Advisors, and Service Delivery Managers to deliver sustainable outcomes. âą Balance speed, quality, cost, security, and maintainability while making clear technical and delivery trade-offs. âą Define success criteria from the outset, including adoption, performance, reliability, risk, cost, and measurable business value. âą Ensure solutions are documented, governed, and transferable so clients can operate them with confidence. âą Turn successful delivery into reusable patterns, accelerators, and building blocks that strengthen future engagements.
âą Typically 5-10 years of relevant experience in AI Engineering, Machine Learning, Software Engineering, Data Engineering, or technical consulting. âą Hands-on experience delivering AI, GenAI, or software solutions into production. âą Experience working directly with clients or in complex stakeholder environments. âą Evidence of turning complex use cases into adopted measurable solutions. âą A degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field - or equivalent practical experience. âą Strong Python development skills, API integration experience, and modern software-engineering practices. âą Hands-on experience with Large Language Models, GenAI architectures, prompt workflows, and model/provider selection. âą Experience with RAG, embeddings, vector search, AI agents, and agentic workflows. âą Familiarity with frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, AutoGen, or comparable tools. âą Experience integrating AI into enterprise systems, APIs, and business workflows. âą Experience with at least one major cloud platform: Azure, AWS, or GCP. âą Working knowledge of Docker, Git, CI/CD, production deployment, monitoring, and evaluation. âą Understanding of MLOps / LLMOps, security, data privacy, governance, and responsible AI principles.
âą Competitive salary âą Flexible working hours âą Professional development budget âą Home office setup allowance âą Global team events
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