Freelance Principal AI Solutions Engineer

Toloka

6d • Remote

About The Position

The Principal AI Solutions Engineer is Toloka's most senior client-facing technical role - the trusted advisor who sits with our clients' most senior technical stakeholders (CTOs and the CTO office, VPs and Directors of ML/Engineering, research and applied-science leads) and turns their model ambitions into rigorous, defensible AI-data strategies. This is a hands-on role: you design and build the solutions that produce the data used to train and evaluate our clients' LLMs - data-generation, labeling, and evaluation pipelines. You are not training the models yourself; you build the data engine that makes them better. You pair executive-grade communication - the clarity and structure you'd expect from a top-tier strategy consultant - with a strong working understanding of how modern LLMs are trained and improved, and the engineering ability to architect and ship the solution yourself. You are not an order-taker: you diagnose the real problem, tell the client what data will actually move their model, and then build the pipeline that produces it - owning the solution end to end, including its commercial outcome. Every engagement is different. One week you might be designing evaluation pipelines for frontier foundation models, the next helping an enterprise build domain-specific reasoning datasets or synthetic data workflows. This role sits at the intersection of consulting, engineering, and AI. You'll work directly with some of the world's leading AI companies, helping shape how next-generation models are trained and evaluated.

Requirements

• Executive communication is your superpower. You can walk into a room with a client's leadership and lead it - clear, concise, structured, persuasive, and calm under pressure. Polished professional English (C1+).
• Strong understanding of modern LLM development, including pre-/post-training, fine-tuning (SFT, RLHF/RLAIF, DPO/PPO, reward modeling, LoRA/PEFT), and evaluation methodologies. Comfortable discussing these topics with senior technical stakeholders and translating their goals into effective AI data solutions.
• Hands-on solution engineering. Experience designing complex AI solutions, building multi-stage pipelines, and developing AI-driven systems (agentic workflows, RAG, synthetic data generation). Strong Python (NumPy, Pandas), API integration, and the ability to prototype LLM-based solutions independently.
• Solid software-development foundations. Version control, testing, MVP thinking, and iterative development. Experience in iterative software development is a big plus.
• Exceptional discovery and problem-framing. Able to translate ambiguous business or research goals into clear AI data and evaluation strategies.
• Seniority and track record. 6+ years delivering complex AI, ML, or data projects end to end. Experience in top-tier strategy consulting (McKinsey, Bain, BCG) and/or as an applied ML / LLM engineer is a strong advantage.
• Ownership mindset. You own outcomes and margins, make trade-offs consciously (time / cost / quality), and stand behind your decisions.

Nice To Haves

• 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.

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 during implementation, uphold reusable standards, and mentor Solution Engineers / Technical Consultants.
• 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.

What This Job Offers

• Career Level: Principal