Responsibilities

• Participate in entire lifecycle of Data Science based projects from design to development, resource planning and support
• Help to build and support ML infrastructure including CICD, data processing pipelines, machine learning workflows both using self supported and managed cloud environments
• Introduce tools for managing features, experiments and payload for ML based services and pipelines
• Research approaches to data processing including batch and streaming, data quality management, data/pipelines orchestration etc to improve efficiency of DS processes
• Support of other tools critical for data science

Qualifications

• Experience in working with any tools and services for managing DS infrastructure (e.g. MLflow, Airflow, Kubeflow, Argo Workflow, DVC or similar)
• Understanding of what ML is, namely the entire ML lifecycle and how to prepare, build, deploy and resign ML Models. Experience with things like Model Registry, Feature store, Experimentation platform, Sync/Async/Batch inference, observability, logging and monitoring
• Deep knowledge of at least one of the clouds, especially in context of Data Science needs

• Knowledge of Python, Golang, Javascript, SQL/noSQL, Async, gRPC, batch/streaming, microservice architecture and ability to learn and use any other instrument within a short period of time is highly recommended

  • Python: FastAPI, Pydantic, Pandas/Scikit-Learn/Numpy, pytest/unittest, mypy/ruff/black/flake8
  • Golang: Kong, huma, testify
• Experience with k8s
• Nice to have experience with LLMs, AI assistants, vibe coding and any other tools to amplify speed of development but with understanding of their limitations and without losing quality
• Nice to have experience with tools for managing data flows (e.g. Spark, Kafka, Presto, Hive, Flink, etc). We don’t expect knowledge of all of them, but it’s necessary to get an idea about what it is, and what it is used for
• Nice to have experience with any of IaC/DevOps/GitOps tools: Terraform/Ansible/ArgoCD/etc
• We also expect that you can explain the background and consequences of your technical decisions to any engineer, prove its correctness and propose alternatives

Conditions & Benefits

• Stable salary, official employment
• Health insurance
• Hybrid work mode and flexible schedule
• Relocation package offered for candidates from other regions
• Access to professional counseling services including psychological, financial, and legal support
• Discount club membership
• Diverse internal training programs
• Partially or fully paid additional training courses
• All necessary work equipment