Applied AI engineer for LLM fine-tuning

Companies increasingly want their own models instead of paying for frontier APIs. A typical case: an enterprise client running a customer-support agent on a proprietary model wants a fine-tuned open-weight model that matches its quality at a fraction onef the cost. You'll own these engagements technically, end to end. You'll sit down with the client's team to understand the use case. You'll recommend the right model, technique and data. Then you'll build it: fine-tuning, evaluation and handover. Behind you is Toloka's expert workforce and data platform, which produce the training and evaluation data.

• Lead technical discovery with client engineering and ML teams. Understand the use case, the current setup (e.g. a prompted frontier-model agent), constraints (cost, latency, privacy, deployment) and what "good" means to them.
• Ask the questions that shape the project: Is fine-tuning the right answer, or would better prompting or retrieval-augmented generation (RAG) do? Which base model and size? SFT, preference tuning, RL or distillation? What data is needed, how much of it, and from whom?
• Turn this into a clear proposal: approach, data plan, evaluation plan, success criteria, risks and timeline.
• Present results and recommendations to client stakeholders, from engineers to engineering leadership.
• Fine-tune open-weight models (Qwen, Llama, Mistral, Gemma, GLM, DeepSeek) with SFT, LoRA/QLoRA, preference optimization (DPO and variants) and RL (PPO, GRPO, RL with verifiable rewards) where justified.
• Distill frontier-model behavior into smaller models: generate and filter teacher data, then fine-tune and close the quality gap.
• Fine-tune for agentic use: tool calling, multi-turn dialogue, following policies and procedures.
• Build evaluation that proves the result: baseline comparison against the client's current model, task-specific test sets, LLM-as-judge and human evals, and regression checks.
• Optimize for production: quantization, serving with vLLM or similar, and cost-per-request and latency analysis.
• Define the data the project needs. Work with our solution engineers and expert workforce to produce it: SFT demonstrations, preference pairs, rubrics, edge cases.

• 4+ years in ML engineering or applied ML research, with at least 2 years hands-on with LLM fine-tuning.
• You have personally fine-tuned open-weight LLMs on GPU infrastructure you managed, beyond hosted fine-tuning APIs. That includes multi-GPU training, LoRA/QLoRA and full fine-tuning.
• Hands-on experience with distillation or with preference or RL methods (DPO, PPO, GRPO or similar).
• Strong evaluation discipline. You can show convincingly whether a fine-tuned model is as good as the baseline, and you know when it isn't.
• Practical knowledge of inference: serving, quantization, and the cost and latency trade-offs between model sizes and frontier APIs.
• Fluent with the stack: PyTorch and Hugging Face (Transformers, TRL, PEFT), plus some of Axolotl, Unsloth, OpenRLHF, verl, vLLM, lm-evaluation-harness or Inspect.
• Client-ready communication. You run a discovery conversation confidently, ask sharp questions, scope a project realistically, and explain technical trade-offs to engineers and non-specialists alike. Fluent professional English (C1+).
• Будет плюсом: You've delivered fine-tuning or ML projects for external clients, or worked in consulting or a client-facing ML role; Experience in regulated domains (finance, insurance, healthcare) and with privacy-preserving data handling; Experience building RAG or agent systems alongside fine-tuning; Public work: papers, open-source contributions or released models (please link them); Mentoring or tech-lead experience; MSc or PhD in ML, CS or a related field

• Full-time independent contractor engagement through the Ontop contractor platform, either as an individual or through your own company or business entity;
• Flexible, fully remote schedule (40 hours per week);
• Work at the leading edge of AI development, alongside a dedicated and dynamic team of experts;
• Projects with customers that are AI industry leaders and well-known household names;
• Friendly community.