[Hiring] Forward Deployed AI Engineer @Prophecy Technologies

Role Description

Most senior client-facing technical role at Toloka. Partner directly with CTOs, VPs of Engineering and ML, and research leaders to solve complex AI data challenges, designing and building the data-generation, labeling and evaluation pipelines that power next-generation AI models, and owning each solution from problem discovery to delivery. The role sits at the intersection of consulting, engineering and AI.

Qualifications

• Executive communication with CTOs, VPs and senior technical stakeholders; professional English (C1+)
• Strong understanding of modern LLM development: how LLMs are trained, fine-tuned and evaluated (SFT, RLHF/RLAIF, DPO/PPO, reward modeling, LoRA/PEFT)
• Post-training experience: SFT and Reinforcement Learning
• Hands-on solution engineering: complex AI solutions, multi-stage pipelines, agentic workflows, RAG, synthetic data generation
• Python (NumPy, Pandas), API integration, and independently prototyping LLM-powered solutions
• Solid software engineering foundations: version control, testing, MVP thinking, iterative development
• Strong discovery and problem framing: translating business or research goals into AI data and evaluation strategies
• 8+ years delivering complex AI, ML or data projects end to end; experience influencing technical direction beyond individual projects
• Ownership mindset: thoughtful trade-offs between time, cost and quality

Requirements

• Act as the primary technical counterpart to CTOs, VPs of Engineering and research/engineering leadership; lead executive conversations with a structured, answer-first (BLUF) approach
• Manage escalations with composure; build long-term trusted relationships through evidence-based recommendations
• Uncover the client's real need (their model, target metrics, how they train or evaluate) and propose the data that will solve it, with options and rationale
• Design and build data solutions hands-on: configure data-labeling components and quality controls
• Develop user interfaces and AI-driven solutions (agentic systems, RAG, synthetic data generation)
• Integrate solutions into automated, multi-stage pipelines that produce data for AI training and evaluation
• Run experiments to prove the pipeline delivers data of the required quality and speed; iterate from MVP toward production
• Provide technical leadership across client engagements, establish reusable engineering standards and mentor Solution Engineers and Technical Consultants
• Own delivery end to end (timelines, quality, scalability) and the commercial outcome, including Gross Margin and Contribution Margin; identify and drive expansion opportunities