Senior Python / MLOps Engineer (Backend & ML Infrastructure)

About the role

Responsibilities

• Technical Leadership & Ownership: Take full ownership of the end-to-end Decision-Making System (DMS) lifecycle, including system architecture, deployment pipelines, production monitoring, continuous code cleanup, and performance optimization.
• Cross-Functional Collaboration: Partner closely with Risk Analysts and Data Scientists to translate complex business logic, risk rules, and ML scoring models (application, behavioral, fraud) into production-ready software.
• Team Leadership & Delivery: Implement core logic of DMS system to suit the needs of InDrive's lending vertical. Code reviews, release management, and performance evaluations while fostering an efficient, agile environment.
• Process Optimization: Reduce time-to-market for credit strategy updates by modernizing delivery workflows, introducing automated regression/retrospective testing, and eliminating dependencies on legacy release cycles.
• Architecture & System Integration: Drive the evolution of our high-volume microservices ecosystem (Golang, AWS). Integrate internal and external services (credit bureaus, collection platforms, fraud detection services) via REST APIs and event-driven pipelines.

Qualifications

• Fintech & Credit Decisioning Expertise: 7+ years of total backend engineering experience, with deep domain expertise in financial technology, credit conveyors, loan processing, debt collection systems, or automated decisioning engines.
• Risk-Ops experience: be familiar with any popular decision-engine (gorules, n8n, camunda, SAS RTDM, FICO Blaze Advisor etc…)
• Stack: Strong expertise in Golang / Python, and build tools (Gitlab CI), AWS-related experience will be a huge plus.
• Data Management: Proficiency with relational and non-relational databases (MySQL, AuroraDB, DynamoDB, GBQ).
• DevOps, CI/CD & Observability: Hands-on experience with modern CI/CD tools (GitLab CI, GitHub Actions), containerization (Docker), and system monitoring tools (Prometheus, Grafana).

Benefits

• Help us challenge injustice by creating fair choices for millions of people across 47 countries.
• Develop your professional skills with access to mentoring, career consulting, and learning programs.
• Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
• Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
• Work alongside people who take initiative, speak openly, and challenge themselves to grow.
• Improve your language skills through co-financed courses and internal speaking clubs.
• Annual salary from 54 200 EUR gross.

Ищем ML-инженера, который умеет не просто обучать модели, а доводить их до production: от данных и feature engineering до деплоя, мониторинга и улучшения качества в реальных системах.

Что будет в работе

• Разработка и развитие ML-моделей: классические ML (классификация, регрессия, ранжирование), deep learning, NLP.

• Проектирование и реализация end-to-end ML-пайплайнов: от данных и feature engineering до обучения, валидации и деплоя.

• Работа с данными: ETL/ELT, подготовка датасетов, feature store, работа с большими объёмами данных.

• Деплой моделей в production: Docker, Kubernetes, REST/gRPC API, batch/online inference.

• Настройка MLOps: MLflow/W&B, версионирование моделей и данных, CI/CD/CT, автоматизация переобучения.

• Мониторинг моделей в production: метрики качества, latency, throughput, data/model drift, алертинг.

• Участие в архитектурных решениях: выбор подходов, оценка trade-off’ов, влияние на стабильность и масштабируемость системы.

• Взаимодействие с data scientists, инженерами, аналитиками и бизнес-стейкхолдерами.

Что важно

• Коммерческий опыт в ML/ML Engineering от 3 лет, уровень Middle+ / Senior.

• Уверенное владение Python:pandas, NumPy, scikit-learn, PyTorch или TensorFlow.

• Опыт построения и деплоя ML-моделей в production (не только Jupyter/прототипы).

• Понимание классического ML: supervised learning, feature engineering, валидация, метрики качества.

• Опыт работы с данными: SQL, ETL/ELT, Spark/Databricks (желательно), работа с большими датасетами.

• Опыт работы с Docker, базовое понимание Kubernetes, CI/CD (GitLab CI, GitHub Actions).

• Опыт настройки MLOps: MLflow/W&B, версионирование моделей/данных, автоматизация пайплайнов (Airflow, Kubeflow, Databricks Workflows).

• Понимание мониторинга моделей в production: метрики, drift detection, алертинг, retraining.

• Умение работать автономно, брать задачу целиком и доводить до результата.

• Умение переводить бизнес-требования в технические решения и аргументировать свой выбор.

Будет плюсом

• Опыт работы с LLM/GenAI: RAG, embeddings, vector databases (FAISS, Milvus, pgvector), prompt engineering, fine-tuning (LoRA)

• Практика LLMOps: MLflow, Langfuse, LangSmith, Weights & Biases, evaluation harnesses (Ragas, DeepEval)

• Опыт работы с agentic systems: LangGraph, CrewAI, multi-agent workflows

• Опыт работы с облачными ML-платформами: AWS SageMaker, Azure ML, Vertex AI

• Опыт работы с feature stores (Feast, Tecton), model registries, A/B testing, canary deployments

• Знание паттернов MLOps: CI/CD/CT, drift detection, rollback, monitoring (Prometheus, Grafana, Evidently, Arize)

• Английский язык для работы с зарубежной документацией, командами и стейкхолдерами.

Английский

• Для проектов без английского язык не требуется.

• Для европейских и американских проектов нужен английский от B2+.

Процесс

• Короткое общение с рекрутером.

• Техническое интервью / TechCertify-скрининг.

• Передача на подходящие проекты клиентам.

Если вам близок инженерный подход к ML, нравится не просто «обучать модели», а доводить их до production и влиять на реальные продукты - будем рады пообщаться.

The role

We are seeking a highly skilled ML/AI Engineer to join our team to lead and support benchmarking of GPU platforms benchmarking of GPU platforms for machine learning and AI workloads. You will play a critical role in evaluating the performance of GPU-based hardware for various deep learning and AI frameworks, enabling data-driven decisions for platform optimisation and next-generation hardware development.

Your responsibilities will include:

• Work closely with hardware, development teams to profile and analyse GPU performance at the system and kernel level.
• Evaluate and compare GPU performance across different platforms, architectures, and software stacks (e.g.,CUDA, ROCm).
• Debug and optimise ML workloads to run efficiently on GPU hardware, identifying and resolving performance bottlenecks.
• Perform acceptance testing acceptance testing for new GPU clusters, ensuring hardware and software meet performance, stability, and compatibility requirements for AI workloads.
• Perform experiments across diverse GPU system configurations to assess the impact of varying interconnect strategies and system-level optimisations on performance and scalability.
• Develop tools and dashboards to visualise performance metrics visualise performance metrics, bottlenecks, and trends.
• Contribute to internal tooling, frameworks, and best practices

We expect you to have:

• A profound understanding of theoretical foundations of machine learning
• Deep understanding of performance aspects of large neural networks training and inference (data/tensor/context/expert parallelism, offloading, custom kernels, hardware features, attention optimisations, dynamic batching etc.)
• Deep experience with modern deep learning frameworks (PyTorch, JAX, Megatron-LM, Tensort-LLM)
• Good understanding of the GPU stack: CUDA,NCCL, drivers, and relevant libraries
• Familiarity with containerized environments (e.g., Docker, Kubernetes).
• Strong communication and ability to work independently

Ways to stand out from the crowd:

• Familiarity with modern LLM inference frameworks (vLLM, SGLang, TensorRT)
• Experience in Python and performance profiling tools (e.g., Nsight, nvprof, perf).
• Familiarity with cloud ML platforms like AWS, GCP, Azure ML
• Contributions to open-source ML benchmarking tools