Freelance Technical Consultant

European UnionRemoteSenior

Technical consultants play a key role in our company, ensuring top-level delivery of our product, its correct configuration, and the deployment of tailored AI data solutions that meet our customers’ needs and deliver an exceptional experience working with us.

As part of this role, you will not only design and implement data labeling pipelines but also act as a trusted technical advisor for our customers - helping them understand their data needs, discover new opportunities, and build scalable AI solutions.

We are looking for a person with more than 3 years of solid technical background, who feels comfortable working in technical environments, solving data-related tasks using Python and AI, and communicating directly with customers. You should be a confident communicator able to engage with both technical and executive stakeholders and collaborate closely with our commercial team.

Responsibilities

1. Client Ownership & Communication

Act as the primary technical point of contact for customers across technical and executive stakeholders.
Lead discovery conversations to deeply understand client goals, problems and quality criteria.
Translate client needs into clear, actionable solution designs and delivery plans.
Advise customers on best practices in data collection, data quality, evaluation, and model training workflows.
Set expectations proactively, communicate progress regularly, and manage risks and trade-offs transparently.
Identify expansion opportunities and additional ways Toloka can create value through data and AI solutions.

2. Solution Design & Delivery

Design end-to-end data solutions using Toloka’s platform, selecting appropriate components, workflows, and quality controls.
Configure data labeling components and quality controls, develop user interfaces and AI-driven tooling (e.g. agentic systems, RAGs, synthetic data generation), and integrate them into end-to-end automated pipelines for AI training and evaluation.
Run experiments and pilots to validate quality, throughput, and cost assumptions.
Lead technical execution during delivery, coordinating internal teams and external stakeholders as needed.
Ensure solutions meet agreed quality, performance, and scalability requirements.

3. End-to-End Ownership & Project Management

Own delivery outcomes holistically - including technical performance, quality, timelines, and financial efficiency.
Monitor key KPIs (e.g. throughput, quality metrics, Gross Margin, Contribution Margin) and act on deviations.
Prioritize work based on customer impact and business value.
Analyze data to identify failure modes, inefficiencies, and opportunities for improvement.
Continuously optimize solutions through iteration, automation, and adoption of new technologies.
Troubleshoot delivery, process, or technical issues in collaboration with support, infrastructure, and product teams.
Drive solutions toward stable operation with minimal ongoing maintenance.

Requirements

Core Experience

Experience as a Solution Engineer, Technical Solutions Engineer, Data Scientist, Data/Systems Analyst, or Technical Project Manager.
Proven experience owning projects end-to-end, including scoping, delivery, and stakeholder management.
Direct experience working with external customers on technical projects (requirements gathering, delivery, iteration).

Technical Skills

Strong Python skills for data manipulation (e.g. NumPy, pandas), natural language processing (e.g. NLTK, spaCy), and building lightweight web services or tools (e.g. FastAPI, Flask).
Solid understanding of ML concepts and data workflows; familiarity with LLMs and prompt engineering is a strong plus.
Experience designing multi-stage pipelines and complex systems.
Understanding of software development fundamentals: version control, testing, MVPs, iterative development etc..
Bachelor’s or Master’s degree in Data Science, ML/AI, Software Engineering, or a related quantitative field such as Physics or Applied Mathematics, with strong analytical foundations.
Ability to reason about trade-offs between quality, speed, cost, and scalability.

Communication & Ownership

Excellent English communication skills (B2+), with the ability to explain complex technical concepts to non-technical audiences.
Confidence working with data engineers, ML engineers, and researchers from leading tech companies.
Strong ownership mindset: comfortable being accountable for outcomes, not just tasks.
Ability to manage multiple projects in fast-paced, ambiguous environments.

Nice to have

Experience with crowdsourcing, expert workflows, or large-scale data labeling.
Experience working on ML training, evaluation, or agentic systems.
Familiarity with quality frameworks, audits, or data validation pipelines.

Responsibilities

1. Client Ownership & Communication

Act as the primary technical point of contact for customers across technical and executive stakeholders.

Lead discovery conversations to deeply understand client goals, problems and quality criteria.

Translate client needs into clear, actionable solution designs and delivery plans.

Advise customers on best practices in data collection, data quality, evaluation, and model training workflows.

Set expectations proactively, communicate progress regularly, and manage risks and trade-offs transparently.

Identify expansion opportunities and additional ways Toloka can create value through data and AI solutions.

2. Solution Design & Delivery

Design end-to-end data solutions using Toloka’s platform, selecting appropriate components, workflows, and quality controls.

Configure data labeling components and quality controls, develop user interfaces and AI-driven tooling (e.g. agentic systems, RAGs, synthetic data generation), and integrate them into end-to-end automated pipelines for AI training and evaluation.

Run experiments and pilots to validate quality, throughput, and cost assumptions.

Lead technical execution during delivery, coordinating internal teams and external stakeholders as needed.

Ensure solutions meet agreed quality, performance, and scalability requirements.

3. End-to-End Ownership & Project Management

Own delivery outcomes holistically - including technical performance, quality, timelines, and financial efficiency.

Monitor key KPIs (e.g. throughput, quality metrics, Gross Margin, Contribution Margin) and act on deviations.

Prioritize work based on customer impact and business value.

Analyze data to identify failure modes, inefficiencies, and opportunities for improvement.

Continuously optimize solutions through iteration, automation, and adoption of new technologies.

Troubleshoot delivery, process, or technical issues in collaboration with support, infrastructure, and product teams.

Drive solutions toward stable operation with minimal ongoing maintenance.

Requirements

Core Experience

Experience as a Solution Engineer, Technical Solutions Engineer, Data Scientist, Data/Systems Analyst, or Technical Project Manager.

Proven experience owning projects end-to-end, including scoping, delivery, and stakeholder management.

Direct experience working with external customers on technical projects (requirements gathering, delivery, iteration).

Technical Skills

Strong Python skills for data manipulation (e.g. NumPy, pandas), natural language processing (e.g. NLTK, spaCy), and building lightweight web services or tools (e.g. FastAPI, Flask).

Solid understanding of ML concepts and data workflows; familiarity with LLMs and prompt engineering is a strong plus.

Experience designing multi-stage pipelines and complex systems.

Understanding of software development fundamentals: version control, testing, MVPs, iterative development etc..

Bachelor’s or Master’s degree in Data Science, ML/AI, Software Engineering, or a related quantitative field such as Physics or Applied Mathematics, with strong analytical foundations.

Ability to reason about trade-offs between quality, speed, cost, and scalability.

Communication & Ownership

Excellent English communication skills (B2+), with the ability to explain complex technical concepts to non-technical audiences.

Confidence working with data engineers, ML engineers, and researchers from leading tech companies.

Strong ownership mindset: comfortable being accountable for outcomes, not just tasks.

Ability to manage multiple projects in fast-paced, ambiguous environments.

Nice to have

Experience with crowdsourcing, expert workflows, or large-scale data labeling.

Experience working on ML training, evaluation, or agentic systems.

Familiarity with quality frameworks, audits, or data validation pipelines.