Tech Lead, Machine Learning (Real-time AI) ›
Software Engineering, Data Science
Prague, Czechia
Posted on Nov 7, 2026
Key Responsibilities
- Own the technical vision and system architecture for machine learning workflows driving Depo Copilot, Depo Summaries, and the Meetings and Notetaking app.
- Architect and build LLM pipelines capable of reasoning over extensive case files and real-time transcripts, ensuring all outputs are fully grounded in source material.
- Determine optimal model strategies - evaluating when custom fine-tuning or trained models are required over prompt engineering - and lead implementation.
- Establish evaluation frameworks alongside our legal experts and annotators, creating custom datasets, rubrics, offline metrics, and release checks to proactively prevent regressions.
- Optimize pipeline efficiency, latency, and operational costs for live streaming analysis using strategic model selection, fallbacks, and precomputation.
- Transition prototypes into production-grade systems with typed contracts, test coverage, tracing, and observability, serving as technical lead for incident response during live proceedings.
- Author design documents, guide architectural decisions, and partner with Product, product engineering, speech recognition, and document retrieval teams to translate roadmap goals into actionable technical plans.
- Mentor machine learning engineers through detailed code reviews, pair programming, and design feedback while clearly communicating technical outcomes and tradeoffs to business stakeholders.
Who are you?
- Minimum of 5 years of experience building production ML or software systems, including at least 3 years in machine learning roles with end-to-end project ownership.
- Proven track record of technical leadership, including conducting design reviews, setting architectural direction, and mentoring engineers.
- Strong background in NLP and production LLM systems, encompassing retrieval and grounding, long-context reasoning, structured outputs, evaluation, and mitigation of common failure modes.
- Experience designing evaluation frameworks for generative AI systems, including developing datasets and rubrics in collaboration with domain experts and annotators.
- Solid software engineering foundations, including API design, data modeling, distributed and asynchronous workflows, testing, and fault tolerance.
- Advanced proficiency in Python, alongside hands-on experience with PyTorch or other deep learning frameworks.
- Experience with real-time/streaming systems or legal domain NLP is a plus.
- Strong written and verbal communication skills in English, a collaborative team spirit, and an autonomous problem-solving mindset.
- Ability to meet periodically at our Prague or Bratislava office to support in-person collaboration.
- Our current stack primarily utilizes Python (DSPy, Pydantic, Temporal), PostgreSQL, Kubernetes, AWS, Google Cloud, and multi-provider LLMs; strong core engineering skills are prioritized over familiarity with specific tools or frameworks.