Note: The job is a remote job and is open to candidates in USA. Toloka AI is a company that creates data powering leading GenAI models and innovations. They are seeking a Principal AI Solutions Engineer who will partner with senior technical leaders to solve complex AI data challenges and deliver scalable solutions, while also shaping best practices in AI solution engineering.
Responsibilities
- Act as the primary technical counterpart to CTOs, VPs of Engineering, and research/engineering leadership
- Lead executive conversations using a structured, answer-first (BLUF) approach
- Manage escalations and expectations with composure and integrity
- Build long-term trusted relationships by recommending evidence-based solutions
- Ask excellent questions to uncover the client's real need - the "question behind the question." Understand their model, which metrics they want to move, and how they intend to train or evaluate it
- Draw on a solid understanding of how LLMs are trained and fine-tuned to have credible conversations with their technical leaders, understand their data strategy, and proactively propose the data that will solve their problem - with options and rationale ("based on your goal, you likely need this, or this")
- Design and build the data solutions yourself: configure data-labeling components and quality controls, develop user interfaces and AI-driven solutions (e.g. agentic systems, RAG, synthetic data generation), and integrate them into automated, multi-stage pipelines that produce data for AI training and evaluation
- Architect and reason about complex, multi-stage solutions end to end; run experiments to prove the pipeline delivers data of the required quality and speed; iterate from MVP toward production
- Provide technical leadership across multiple client engagements, establish reusable engineering standards and best practices, and mentor Solution Engineers and Technical Consultants to raise the overall technical bar of the organization
- Own delivery end to end - timelines, quality, and scalability - and the commercial outcome: Gross Margin and Contribution Margin, with the levers, trade-offs, and next checkpoints named, not just described
- Identify and drive expansion opportunities
Skills
- Executive communication. You can confidently lead conversations with CTOs, VPs, and senior technical stakeholders - clear, concise, structured, persuasive, and calm under pressure. Professional English (C1+) is essential
- Strong understanding of modern LLM development. You understand how modern LLMs are trained, fine-tuned, and evaluated (including SFT, RLHF/RLAIF, DPO/PPO, reward modeling, and LoRA/PEFT). You're comfortable discussing these topics with senior technical stakeholders and translating their goals into effective AI data solutions
- Hands-on solution engineering. You've designed complex AI solutions, built multi-stage pipelines, and developed AI-driven systems (e.g. agentic workflows, RAG, or synthetic data generation). You're comfortable working in Python (NumPy, Pandas), integrating APIs, and independently prototyping LLM-powered solutions
- Solid software engineering foundations. You understand version control, testing, MVP thinking, and iterative development. Experience building production software is a strong plus
- Exceptional discovery and problem framing. You enjoy working through ambiguity, asking the right questions, and translating business or research goals into clear AI data and evaluation strategies
- Seniority and track record. 8+ years delivering complex AI, ML, or data projects end to end, with experience influencing technical direction beyond individual projects. Experience establishing engineering standards, mentoring engineers, or leading cross-functional technical initiatives is highly valued. Experience in top-tier strategy consulting (McKinsey, Bain, BCG) and/or as an applied ML / LLM engineer is a strong advantage
- Ownership mindset. You take responsibility for outcomes, make thoughtful trade-offs between time, cost, and quality, and are comfortable owning both technical and commercial success
- Hands-on experience training or fine-tuning LLMs and/or building agentic systems (helps you reason about client needs - though the role itself is about building data solutions, not training models)
- Advanced degree (MSc/PhD) in AI, CS, or a related field
- Experience in crowdsourcing and/or data-centric AI, and with fast-paced, multi-project delivery for frontier AI clients
Company Overview