Machine Learning Engineer

As a Machine Learning Engineer at Xomnia, you will build AI systems that make it out of the notebook. Feature pipelines, model serving, evaluation, monitoring, CI/CD: the engineering that turns a promising model into something a client's team actually uses every day. Part of that work is classical ML. A growing part of it is LLM-based: RAG pipelines, agentic workflows, and GenAI applications running in production.

You do this as a consultant, which means you carry both the engineering and the conversation with the people who have to work with what you build. See yourself doing this? Then read along!

• Take models from experiment to production: feature engineering, data pipelines, model serving, monitoring, and CI/CD.

• Build GenAI applications that hold up in production: retrieval, agentic workflows, evaluation sets, guardrails, and cost and latency budgets that the client can live with.

• Choose the right tool for the problem. Sometimes that is an LLM. Often a forecasting model, a recommender or an anomaly detector is the better answer, and part of your job is saying so.

• Deploy on Azure, AWS or GCP, using managed AI platforms such as AWS Bedrock or Microsoft AI Foundry where they fit.

• Work with the client team, not next to it: translate what the business needs into something buildable, and explain the trade-offs you made.

• Help clients build their own capability, through code reviews, pairing and engineering practices that outlast your assignment.

• Share what you learn internally through Learning Labs, Xpert sessions and mentoring.