Senior ML Engineer (LLMOps)

We are looking for an aspiring professional to join our RnD team. Responsibilities Design, build, and maintain production-grade ML systems with a strong focus on Large Language Models (LLMs). Own and evolve the LLMOps lifecycle: data preparation, fine-tuning, evaluation, deployment, monitoring, and iteration. Develop evaluation frameworks for LLM quality, robustness, and regression tracking. Collaborate closely with researchers, product engineers, mathematicians, and infrastructure teams to translate research prototypes into reliable production systems. Contribute to architectural decisions around agentic systems, RAG pipelines, and hybrid ML + symbolic components. Experience 3+ years of experience in Machine Learning or Applied AI roles. Hands-on experience deploying ML models to production environments. Practical experience with LLMs (open-source or proprietary) in real-world applications. Experience operating ML systems under production constraints (latency, cost, observability, reliability). Technical Skills Strong Python proficiency; experience with ML frameworks. Solid understanding of modern LLM stacks: fine-tuning, inference optimization, RAG, prompt/agent orchestration. Experience with MLOps / LLMOps tooling: experiment tracking, evaluation pipelines, monitoring, CI/CD for models. Familiarity with containerization and deployment (Docker, Kubernetes or equivalents). Experience with cloud or on-prem GPU environments. Nice to haves Experience working in fast-moving startup or R&D-driven environments is a strong plus. Understanding of distributed systems concepts is a plus. Educational Background Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or a related field (or equivalent practical experience). Soft Skills Proactive mindset to stay updated with the latest advancements in AI. Fluent in conversational and written business English (C1+). Ability to work collaboratively in cross-functional teams. Experience working using Agile framework. Personal Qualities Individual responsibility. You respect key deadlines and pass on the results of your work to your teammates in an appropriate condition. Lifelong learning. You recognize areas for growth and proactively learn new skills for your current and prospective areas of responsibility. Vision & planning. You can plan your work several weeks ahead and can juggle multiple projects at once. You know when to postpone a task. Thoroughness. You cover every important aspect of your task leaving out no crucial detail. Proactiveness and initiative . You offer help if you have spare capacity. You take initiative and pitch your own projects to others. Critical thinking. You question every judgement, claim or number and can engage in a healthy debate with your teammates. Dynamic, out-of-the-box mindset. You can challenge existing ways, abandon well-trodden paths and embrace the new. Hiring process steps Initial Screening Interview (45 minutes)  With either CHRO or Talent Specialist. Test Assignment  This mandatory assignment takes up to 2 hours to complete, within 24 hours from the start, and is unpaid. 1st Technical Interview with our Tech Lead (1 hour)  Discuss the test assignment, and check Python proficiency. 2nd Technical Interview with our LLM Lead (2 hours)  To check domain specific proficiency.  Final interview with our CEO (1.5 hours)  To check values alignment.