AI Engineer

Umain•Chicago, IL

9h•$130,000 - $170,000•Hybrid

About The Position

Umain, part of Eidra, is a digital product consultancy with nearly 200 engineers, designers, and product strategists. We partner with companies including McDonald’s, Polestar, Heineken, Rijksmuseum, Spotify, and Philips Hue to shape, build, and scale the products and platforms their businesses and customers rely on. We work in small, senior teams, staying with a product from its first idea through launch and beyond. Umain is growing its AI engineering team in the US. Our AI engineers around the world design, prototype and build AI-powered tools and software that change how clients work, from advanced GenAI workflows and custom agents to production-ready AI applications. You'll join a global team of more than 30 AI engineers across Sweden, the Netherlands, Norway, and North America, working alongside strategy consultants, marketers, data experts and designers. This full time role is based in Chicago at our West Loop office. As part of Umain's expanding U.S. footprint, you'll help establish our reputation for engineering excellence, strategy, and delivery in the American market. You must be in Chicago or willing to relocate to be considered for this role as we require a minimum of 3 days in-office per week. As an AI Engineer at Umain, you’ll build the next generation of AI-powered products, workflows, and agents for our clients, combining strong software engineering fundamentals and full-stack product thinking with modern AI capabilities. Your work will range from integrating LLMs and vision models to architecting agents, building scalable pipelines and experimenting with new techniques. You’ll turn ambiguous ideas into robust solutions, evaluate where AI truly adds value and help clients understand what is possible, feasible, and responsible—all while playing an active role in shaping our growing AI practice.

Requirements

Approximately 8 years engineering experience that includes at least 4 years building AI-powered systems, or equivalent hands-on experience shipping AI in production systems.
Hands-on experience with LLMs, embeddings, vector databases, prompt and context engineering, retrieval setups, fine-tuning, and model evaluation.
Experience building agents, including memory, tool use, and orchestration, with at least one modern agent framework.
Experience integrating AI services such as OpenAI, Azure OpenAI, Anthropic, Google Gemini, and Meta models.
Strong software engineering skills in TypeScript and/or Python, including real backend systems and data pipelines.
Full-stack product thinking: frontend or backend experience outside the traditional AI field, and the ability to build end to end.
Proven experience deploying and running scalable applications in Azure, GCP, or AWS.
Experience with Docker, CI/CD, and modern DevOps practices. You can take a solution from "it works on my laptop" to "it runs reliably in the cloud."
A track record of delivering AI solutions, not just prototypes.
A pragmatic mindset and the judgment to know when AI is the right tool and when it isn't.
The ability to turn vague questions into concrete technical solutions for our clients.
Strong communication skills and comfort working in multidisciplinary teams.
Curiosity about emerging AI capabilities, creativity in applying them, and enthusiasm for exploring new approaches.

Nice To Haves

Experience with machine learning, computer vision, generative images, and/or multimodal models.
Experience in consultancy or client-facing environments.

Responsibilities

Partner with clients to clarify their challenges, decide whether AI is the right answer and assess feasibility and risk, including data security, privacy, licensing, and responsible AI.
Design, prototype, build, and ship AI-powered applications, workflows, and agents for clients ranging from start-ups to global enterprises.
Architect agentic systems, including context management, memory, orchestration, and the tools, skills, and MCP integrations agents rely on.
Select, integrate, and adapt foundation models (OpenAI, Anthropic, Google, and others). Fine-tune or combine them when off-the-shelf options fall short.
Design around model limitations in both the architecture and the product's UX. Apply techniques like retrieval, chunking, embeddings, and redlining, and debug AI systems when they misbehave.
Build evaluation and observability into everything you ship. Use stochastic test cases, trace-based analysis, and tools to benchmark and monitor models, prompts, and agents.
Build the software, data pipelines, and cloud infrastructure (Azure, GCP, or AWS) that keep AI products running reliably at scale.
Own parts of project delivery, communicate clearly with technical and non-technical stakeholders, and lead client and internal workshops.
Bring technical credibility to new business. You'll join pitches as our AI expert, shape proposals and RFP responses, and help prospective clients see what's possible.
Help define Umain's AI offerings and grow the competence through internal frameworks, playbooks, knowledge sessions, and engineering standards.