Freelance Principal AI Solutions Engineer

Role Description

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.

What you'll do

• Executive partnership
  • 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.
• Data strategy & needs diagnosis
  • 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.
  • Proactively propose the data that will solve their problem - with options and rationale.
• Solution engineering & delivery (hands-on)
  • Design and build the data solutions yourself: configure data-labeling components and quality controls, develop user interfaces and AI-driven solutions, and integrate them into automated, multi-stage pipelines.
  • 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.
• Commercial ownership
  • Own delivery end to end — timelines, quality, and scalability - and the commercial outcome: Gross Margin and Contribution Margin.
  • Identify and drive expansion opportunities.

Qualifications

• Executive communication is your superpower.
• Strong understanding of modern LLM development, including pre-/post-training, fine-tuning, and evaluation methodologies.
• Hands-on solution engineering experience designing complex AI solutions.
• Strong Python (NumPy, Pandas), API integration, and the ability to prototype LLM-based solutions independently.
• Solid software-development foundations.
• Exceptional discovery and problem-framing skills.
• 6+ years delivering complex AI, ML, or data projects end to end.
• Ownership mindset.

Nice to have

• Hands-on experience training or fine-tuning LLMs and/or building agentic systems.
• Advanced degree (MSc/PhD) in AI, CS, or a related field.
• Experience in crowdsourcing and/or data-centric AI.

Important Notice

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