Forward Deployed AI Engineer

Keyrus Group•Chicago, IL

2d•Remote

About The Position

As a Forward Deployed AI Engineer, you work at the heart of a client's most pressing AI challenges, turning intent into an operational, measurable result in weeks rather than months. This is an experienced individual-contributor role for someone who combines hands-on engineering, architectural judgment, and business understanding. You own the problem from ambiguity through to execution - understanding the real context, building and deploying the solution, and proving, not declaring, that it creates value. Once the terrain is understood, you become the reference the team relies on to make that result last. Keyrus is an international group of 2,800 consultants and experts across 28 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have been building the data foundations that make intelligent systems work - designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalise intelligence. At Keyrus, you will not simply develop technical skills. You will strengthen the judgment required to understand complex environments, make sound decisions, and design systems that create sustainable value. Over time, you grow into a professional who bridges data, AI, and human decision-making at scale - a Keyrus Architect of Intelligence. Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.

Requirements

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.

Nice To Haves

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.

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.