Старший инженер по машинному обучению (Python)

Обязанности:
-Разработка и внедрение моделей машинного обучения и глубокого обучения (ML/DL) для бизнес-задач.
-Оптимизация и поддержка существующих алгоритмов.
-Работа с большими данными: очистка, подготовка, анализ.
-Интеграция моделей в продакшн (API, микросервисы).
-Исследование новых подходов в области AI (LLM, генеративные модели).
Требования:
-Опыт работы от 2 лет в области ML/AI.
-Знание Python и библиотек: TensorFlow, PyTorch, scikit-learn.
-Опыт работы с SQL и NoSQL базами данных.
-Понимание архитектуры нейронных сетей, NLP, CV.
-Английский язык — уровень B2 и выше.
Будет плюсом:
-Опыт работы с облачными платформами (AWS, GCP, Azure).
-Знание MLOps и CI/CD.
-Участие в AI-хакатонах или open-source проектах.

Andersen is hiring an ML Engineer (Routing) for a project developing machine learning solutions for real-time routing optimization, ETA prediction, and large-scale geospatial data processing.

The customer is a global investment management firm providing tailored investment solutions to institutional and private clients. It combines financial expertise with long-term investment strategies to help clients achieve their objectives while adapting to changing market conditions. The organization focuses on innovation, responsible investing, and operational excellence, continuously enhancing its capabilities to deliver sustainable value and support long-term growth.

The project is focused on developing machine learning solutions to improve ETA accuracy and routing quality at a scale. It includes building production-grade ML models, processing large-scale geospatial data, and deploying low-latency inference systems for real-time routing optimization.

• Designing and building ML models that correct and refine the routing engine's ETA estimates, from gradient-boosted trees through to neural and Transformer-based architectures as data scale grows.
• Developing traffic-estimation models that turn large-scale GPS data into road-level speeds and historical-traffic profiles and feed them into the routing engine to produce time-of-day-aware ETAs.
• Working on map-matching that snaps noisy GPS data onto the road graph, and the spatial aggregations that make movement and speed data usable for modeling.
• Improving ETA calculation, smoothing, and rerouting logic to close the gap between predicted and actual arrival times across changing conditions.
• Translating routing and ETA goals into ML objectives with the right proxy metrics and non-functional requirements, including loss formulations where under- and over-prediction carry different costs.
• Leading evaluation end-to-end, from offline accuracy and routing-quality metrics to the design of online experiments including shadow tests and interference-aware designs such as switchbacks and prove a change improves accuracy before it ships.
• Partnering with backend engineers to take models from prototype to low-latency production serving that meets tight latency and throughput targets.
• Partnering with product and operations to turn routing and traffic analysis into concrete features and requirements and help extend ETA capabilities across verticals.
• Owning the ML lifecycle in production – serving, monitoring data and concept drift, and building the retraining pipelines that hold quality as traffic patterns, cities, and the map shift.

• Experience in machine learning engineering for 5+ years, including at least 3+ years building and deploying deep learning models in production.
• Direct experience with regression, forecasting or prediction models on tabular, time series or event data (e.g. delivery time, demand, pricing, logistics), including gradient boosting (XGBoost, LightGBM, CatBoost).
• Expert-level proficiency in Python and SQL, along with core data science libraries (PySpark, Pandas, NumPy, Scikit-learn, PyTorch).
• Ability to design an ML system from scratch, covering data analysis, data processing and feature engineering from raw logs through to model serving in production.
• Experience turning a business goal into an ML problem with the right proxy metrics and non-functional requirements, and designing or substantially contributing to A/B tests, switchback or shadow experiments and statistical evaluation that prove business impact.
• Experience using MLOps tools and practices to manage the full ML model lifecycle, including monitoring and automated retraining.
• Experience deploying models on ML serving infrastructure with a focus on low latency, and practical knowledge of how to detect and manage concept drift.
• Level of English – from Upper-Intermediate and above.

• Subject matter depth in ETA / travel-time prediction, traffic estimation, or routing-engine quality.
• Hands-on experience with open-source routing engines (e.g., Valhalla, OSRM, GraphHopper) and concepts such as map-matching, speed profiles, road-graph tiles, and historical traffic.
• Experience in mapping, location, or geospatial products.
• Experience building for developing markets, where the underlying map and address data is weak.
• Experience with cloud data and ML platforms such as BigQuery or Databricks (certifications is a plus), and with distributed deep-learning training.

• Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc..
• The opportunity to change the project and/or develop expertise in an interesting business domain.
• Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant.
• Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee.
• The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
• Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
• Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies).
• Certification compensation (AWS, PMP, etc).
• Referral program.
• Private health insurance and sports compensation, depending on the type of employment.

Join us!

Andersen is hiring a Senior Ontology Engineer for a project building a large-scale Knowledge Graph and delivering AI-powered data and identity solutions.

The company is a global technology provider delivering data analytics and AI-powered solutions that help organizations better understand customer behavior, optimize marketing performance, and make informed business decisions. By transforming large volumes of data into actionable insights, it enables enterprises to improve audience engagement, measure the effectiveness of their initiatives, and enhance decision-making. The company works with a diverse range of customers, supporting them in navigating an increasingly data-driven and rapidly evolving digital landscape.

The project is focused on building a large-scale Knowledge Graph and Identity platform that models relationships between audiences, content, brands, devices, and behavioral data. It combines graph technologies, big data processing, and AI-powered enrichment to support identity resolution, audience intelligence, measurement, and advanced analytics.

• Owning the end-to-end design, development, and versioning of core RDF/RDFS/OWL ontologies and SHACL validation frameworks.
• Defining ontology standards, derived-attribute schemas, aggregation/scoring logic, and governance practices aligned with W3C and relevant industry standards.
• Leading ontology design reviews and translating business requirements into scalable knowledge graphs and semantic data models.
• Co-designing derivation and transformation pipelines with Data Engineering using Databricks/Spark, including schemas and validation checkpoints.
• Building scalable, production-grade knowledge graph pipelines using Python and SPARQL, including entity resolution, record linkage, and data enrichment.
• Defining graph vs. data lake strategies to balance query performance, storage, scalability, and refresh costs.
• Applying embeddings and LLM-based approaches to ontology mapping, entity disambiguation, semantic similarity, and GraphRAG use cases.
• Collaborating with Data Engineering, Data Science, Platform, and Product teams to ensure production-ready graph solutions.
• Mentoring Ontology Engineers and junior specialists and leading technical workshops on ontologies, knowledge graphs, and semantic technologies.

• Bachelor's degree in Computer Science, Information Science, Computational Linguistics, Mathematics, or related fields.
• Hands-on experience in ontology engineering, semantic data modeling, or knowledge graph development for 5+ years, with a demonstrable track record of production ontologies at scale.
• Deep expertise in W3C semantic web standards: RDF, RDFS, OWL, SPARQL 1.1, and SHACL.
• Hands-on experience building and validating graph schemas in a production triplestore (Amazon Neptune, Stardog, GraphDB, Jena, or equivalent).
• Strong Python, including production-quality, well-tested code; comfortable building data pipelines and graph processing workflows.
• First-principles understanding of description logics, ontology design patterns, and the practical trade-offs between OWL expressivity and triplestore scalability.
• Hands-on experience with entity resolution, record linkage, or deduplication at scale (mapping messy, multi-source real-world data to clean ontological representations).
• Strong communication skills, ability to defend ontological modeling decisions in design reviews and explain trade-offs to non-specialist stakeholders.
• Level of English – from Intermediate+ and above.

• Master's degree or PhD in Computer Science, Information Science, Computational Linguistics, Mathematics, or related fields.
• Hands-on experience with Amazon Neptune or Stardog, including data virtualization (Neptune Orion or Stardog Virtual Graphs) over data lake sources.
• Experience designing aggregation and derivation logic that converts raw behavioral event data into durable, graph-resident derived attributes.
• Domain knowledge in media, entertainment, or ad tech (TV viewership (ACR/STB), digital audience modeling (device graphs, identity resolution), or ad exposure data).
• Familiarity with industry content and identity schemas: EIDR, Schema.org VideoObject, DDEX, or equivalent.
• Experience with embedding models, vector databases (Milvus, Pinecone, Weaviate), and GraphRAG architectures (LangChain/LlamaIndex).
• Familiarity with GNN-based approaches to knowledge graph reasoning or entity resolution.
• Working knowledge of PySpark and Databricks for large-scale transformation pipelines.

• Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc..
• The opportunity to change the project and/or develop expertise in an interesting business domain.
• Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee.
• The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
• Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
• Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies).
• Certification compensation (AWS, PMP, etc).
• Referral program.
• Private health insurance and sports compensation, depending on the type of employment.

Join us!