Applied Scientist / MLOps Engineer (Data Anonymization / Privacy AI)

IT Vacancies A key role in developing an AI product from scratch, working with Big Data and cutting-edge technologies in data anonymization and Privacy AI. We are looking for a leader ready to head the direction and be responsible for the results.

Format: remote

Salary Level: from 1200 to 1500 rubles per hour

Employment: full-time

🖥 Format

Remote

🕹 Employer

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🕹 List of Responsibilities:

— Development of a data classifier for database fields (Tabular ML)

— Development of an existing ML model (RandomForest) for detecting PII in database fields

— Training the model to recognize new data types (VIN numbers, specific identifiers)

— Improving classification quality, fixing errors

— Solving the problem of "mixed data"

— Optimizing the model for high-speed CPU operation

— Creating a model for detecting data in arbitrary text (NLP / NER)

— Development of architecture and training of an NER model for finding PII in unstructured text

— Working with various file formats: TXT, DOCX, PDF, and logs

— Implementation of pipelines for cleaning and normalizing "noisy" data

🕹 Candidate Requirements:

— Experience in developing and implementing ML models in production for 3+ years

— Experience in managing the full cycle: from research and task definition to quality monitoring and automatic retraining setup

— Readiness to lead the direction, independently decompose business tasks

— Confident Python skills (pandas, numpy, scikit-learn)

— Deep understanding of classical ML algorithms (decision trees, ensembles, Random Forest)

— Practical experience with Hugging Face Transformers and/or SpaCy

— Mandatory experience in training NER models

— Experience in building unstructured data processing pipelines (PDF, texts)

— Feature Engineering skills for tabular data

— Experience in optimizing model inference for CPU operation

— Experience working with large volumes of data (full database scans of several TB)

— Experience deploying models via API (FastAPI), containerization (Docker)

— Proficiency in versioning tools (MLflow, DVC)

— Understanding of CI/CD processes

— Experience in setting up model quality monitoring (Model Drift, Quality Gates) and organizing automatic retraining

— Will be a plus: experience with classifiers for database data profiling

— Will be a plus: experience in recognizing specific data types (VIN, card numbers, crypto addresses)

— Will be a plus: experience in personal data protection, data anonymization, or anti-fraud systems

🕹 Approximate Salary Level:

from 1200 rubles per hour to 1500 rubles per hour

🕹 Working Conditions:

— full-time

— Registration under an individual entrepreneur contract

— Opportunity to influence the architecture of a complex AI product from scratch

— Work with real Big Data tasks (TB+)

— Modern stack and no legacy

🕹 Contacts:

recruiter contact