# ML/Data Engineer

**Company:** [Intetics](https://jobs.workable.com/companies/gDvZSRWtedxcHZawvZWQAz.md)
**Location:** Remote
**Workplace:** remote
**Department:** Machine Learning

[Apply for this job](https://jobs.workable.com/view/8071b2a8-dea3-45c2-b44d-c6481e32aafc)

## Description

**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.