Note: The job is a remote job and is open to candidates in USA. Keyrus is an international group focused on operationalising intelligence in business processes. As a Forward Deployed AI Engineer, you will tackle clients' AI challenges, transforming intent into measurable results through hands-on engineering and architectural judgment.
Responsibilities
- Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on delivery
- Locate, qualify, and secure access to the data required for each use case, working directly with the Data Engineer
- Translate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows
- Prototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain experts
- Work with Data Engineers, Software Engineers, Foundations Architects, Governance experts, Business Value Advisors, and Service Delivery Managers to deliver sustainable outcomes
- Balance speed, quality, cost, security, and maintainability while making clear technical and delivery trade-offs
- Define success criteria from the outset, including adoption, performance, reliability, risk, cost, and measurable business value
- Ensure solutions are documented, governed, and transferable so clients can operate them with confidence
- Turn successful delivery into reusable patterns, accelerators, and building blocks that strengthen future engagements
Skills
- Typically 5-10 years of relevant experience in AI Engineering, Machine Learning, Software Engineering, Data Engineering, or technical consulting
- Hands-on experience delivering AI, GenAI, or software solutions into production
- Experience working directly with clients or in complex stakeholder environments
- Evidence of turning complex use cases into adopted measurable solutions
- A degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field - or equivalent practical experience
- Strong Python development skills, API integration experience, and modern software-engineering practices
- Hands-on experience with Large Language Models, GenAI architectures, prompt workflows, and model/provider selection
- Experience with RAG, embeddings, vector search, AI agents, and agentic workflows
- Familiarity with frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, AutoGen, or comparable tools
- Experience integrating AI into enterprise systems, APIs, and business workflows
- Experience with at least one major cloud platform: Azure, AWS, or GCP
- Working knowledge of Docker, Git, CI/CD, production deployment, monitoring, and evaluation
- Understanding of MLOps / LLMOps, security, data privacy, governance, and responsible AI principles
- Experience with multimodal models, fine-tuning, model adaptation, or open-source LLMs
- Front-end or full-stack development experience, for example, Node.js or React
- Consulting or professional-services experience
- Exposure to regulated industries or enterprise governance requirements
Company Overview
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