ML/Data Engineer
# ML/Data Engineer **Intetics** · Romania · `Remote` · `Full Time` 💼 **Уровень роли:** `Mid-level` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Data-Engineer]` `[Machine-Learning-Engineer]` `[Data-Infrastructure-Engineer]` `[Data-ML-Engineer]` `[AI-ML-Data-Engineer]` `[Machine-Learning-Data-Engineer]` `[Data-and-ML-Engineering]` `[Data-and-AI-ML-Engineering]` --- ### About the Role Intetics Inc. , a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced ML/Data Engineer to join our dynamic team on a full-time basis. Customer location: USA Candidate locations: EU ONLY Working hours: 11.00-20.00 CET Language: English B2+ Estimated start date: 16.11.2026 Duration: 12 months Project Responsibilities - Design and build production data infrastructure for high-volume athlete performance and sensor data. - Build and operate real-time and near-real-time ingestion systems for streaming data. - Design storage and data architectures for high-volume time-series data and longitudinal athlete records. - Build infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency. - Design and implement graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes. - Build data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases. - Design systems that maintain strong tenant-level data isolation across clubs and customers. - Establish appropriate data provenance, lineage, auditability, and observability across the platform. - Work with ML and AI engineers to provide reliable data foundations for model training, inference, and evaluation. - Work with sport scientists and domain experts to translate complex requirements into durable production systems. - Make pragmatic technology and architecture decisions as the platform evolves. Requirements - 5+ years of full-time professional software or data engineering experience, excluding internships, university placements, coursework, and academic projects. - Proven experience designing, building, and operating production data infrastructure at scale. - Strong experience working with time-series data or time-series databases, such as InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent technologies. - Significant experience with real-time or streaming data ingestion, using technologies such as Kafka, Kinesis, Flink, Spark Streaming, Pulsar, or equivalent. - Experience designing graph data models or graph database schemas, not simply querying or consuming an existing graph database. - Experience designing or operating multi-tenant systems with tenant-level data isolation. - Strong Python and SQL skills. Professional experience with Go is highly desirable. - Experience working with production systems where reliability, scalability, observability, and data correctness matter. - Ability to take ownership of ambiguous technical problems and turn them into practical production architectures. - Experience working directly with data scientists, ML engineers, or other technical domain specialists. - Experience building probabilistic evaluation, model calibration, or model monitoring infrastructure. - Experience with causal inference, counterfactual modelling, or simulation. - Experience working with wearable sensors, IoT data, biomechanics, sports technology, or other high-frequency telemetry. - Experience building knowledge graphs or domain-specific ontologies. - Experience with LLM or AI evaluation frameworks and an understanding of their limitations. - Experience with AWS, including ECS, EC2, Lambda, SNS, SQS, or related services. - Experience with GraphQL, REST, gRPC, Postgres, MongoDB, or similar technologies. Originally posted on Himalayas
- Data-Engineer
- Machine-Learning-Engineer
- Data-Infrastructure-Engineer
- Data-ML-Engineer
- AI-ML-Data-Engineer
- Machine-Learning-Data-Engineer
- Data-and-ML-Engineering
- Data-and-AI-ML-Engineering
Наблюдалась 2026-10-17, впервые 2026-10-17, источник — Himalayas (JSON API).