About this opportunity
Role Overview
Toloka AI is seeking a Technical Consultant to design and deliver AI data solutions for customers. You will build data labeling pipelines, configure technical solutions, and act as a technical advisor to clients on data quality, AI training, and evaluation workflows.
Responsibilities
Serve as the primary technical contact for customers and lead technical discovery conversations.
Translate customer requirements into solution designs and delivery plans.
Advise clients on data collection, quality, evaluation, and model training workflows.
Design and configure end-to-end data labeling and AI data solutions.
Build and integrate AI-driven tools, including agentic systems, RAGs, and synthetic data solutions.
Run experiments and pilots to validate quality, throughput, and cost.
Own delivery outcomes, including technical performance, quality, timelines, and financial efficiency.
Monitor KPIs and optimize solutions for quality, speed, cost, and scalability.
Troubleshoot technical and delivery issues with internal and external teams.
Identify opportunities to expand customer value through data and AI solutions.
Requirements
3+ years of relevant technical experience.
Experience as a Solution Engineer, Technical Solutions Engineer, Data Scientist, Data/Systems Analyst, or Technical Project Manager.
Experience owning technical projects end-to-end and working directly with customers.
Strong Python skills, including data manipulation and relevant tools such as NumPy, pandas, NLTK, spaCy, FastAPI, or Flask.
Strong understanding of ML concepts, data workflows, and complex multi-stage systems.
Understanding of software development fundamentals, including version control, testing, MVPs, and iterative development.
Bachelor’s or Master’s degree in Data Science, ML/AI, Software Engineering, Physics, Applied Mathematics, or a related quantitative field.
Ability to balance quality, speed, cost, and scalability.
Excellent English communication skills (B2+).
Ability to manage multiple projects in a fast-paced environment.
Nice to Have
Familiarity with LLMs and prompt engineering.
Experience with crowdsourcing, expert workflows, or large-scale data labeling.
Experience with ML training, model evaluation, or agentic systems.
Familiarity with quality frameworks, audits, or data validation pipelines.
