Machine Learning Engineer

Arionkoder is an AI consulting firm helping companies build, embed, and own AI systems that drive real business outcomes. We combine Product Development, Artificial Intelligence, and Team Augmentation to craft digital products and solutions that create lasting impact — guided by a human-centric approach and a commitment to pushing the boundaries of what technology can do.

We are looking for a Senior Machine Learning Engineer to own LLM-powered systems end-to-end - from a first proof of concept built in days, to a production system a client trusts with their core workflows. You will work inside AI Labs, where most engagements start as an ambiguous business problem (complex legal documents, scanned and digital; extraction, classification, assistants, agents) and end as a working system with measurable quality.

This is a hybrid of engineer and scientist. You write production-grade Python and ship it through CI/CD, and you treat model quality as an engineering discipline: you do not say "it works" — you show the eval. Curated datasets, graded criteria, baselines, regression thresholds wired into CI. You know that an LLM feature without evals is an untested feature, and you build the measurement before you build the demo.

You are vendor-agnostic and cost-aware: you choose between a prompt, a RAG pipeline, a fine-tune, a classical model, or plain deterministic code based on the problem, the latency budget, and the cost per request - not on what is fashionable. You use AI tools daily to accelerate your own work, with the judgment to know when to trust the output and when not to.

Because we are a consultancy, you are also client-facing: you demo live, explain trade-offs to non-technical stakeholders, size effort honestly in pre-sales, and turn what you learn into assets the rest of the company can reuse. This is not a role for someone who waits for a well-specified ticket. We need an engineer who challenges the framing of the problem, says when the data does not support the ask, and treats production confidence as a deliverable.

Required Qualifications

• 5+ years of professional software engineering experience, with production ownership — not notebooks handed off to someone else.
• 2+ years building LLM-powered systems in production or in delivered client engagements: prompt engineering, structured extraction, RAG, workflows, or agents.
• Experience designing and running evals: building eval datasets, defining graded criteria, establishing baselines and regression thresholds, and using them as a release gate. Candidates who have only eyeballed outputs will not pass this bar.
• Advanced Python: clean, tested, reviewable code; async, typing, packaging, dependency management.