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.