ML/Data Engineer

Intetics-Inc; United States; Remote; USD 90.000 - 130.000; Vollzeit

Zusammenfassung

Intetics Inc. is hiring a skilled ML/Data Engineer for a full-time, 12-month project in the USA. You will design and operate large-scale data infra, real-time ingestion, and graph models for athlete data, ensuring low-latency ML foundations and strong data provenance. Collaboration with ML engineers and domain experts is essential.

The role requires extensive Python/SQL, experience with time-series data, and multi-tenant systems. Remote-friendly with CET hours and EU candidate emphasis.

Qualifikationen

• 5+ years of full-time professional software or data engineering experience, excluding internships.
• Proven experience designing, building, and operating production data infrastructure at scale.
• Strong experience with time-series data or time-series databases.
• Significant experience with real-time or streaming data ingestion technologies.
• Experience designing graph data models or graph schemas.
• Experience operating multi-tenant systems with tenant-level isolation.
• Strong Python and SQL skills; Go is highly desirable.
• Experience with reliable, observable, scalable production systems.
• Ability to own ambiguous technical problems and translate to production architectures.
• Experience working with data scientists/ML engineers.

Aufgaben

• Design and build production data infrastructure for high-volume athlete data.
• Build and operate real-time and near-real-time ingestion systems.
• Design storage architectures for high-volume time-series data.
• Make production features and metrics available to ML systems with low latency.
• Design graph data models representing athletes, training loads, and outcomes.
• Build ML evaluation infrastructure for model calibration and monitoring.
• Maintain tenant data isolation across clubs and customers.
• Establish data provenance, lineage, and observability across the platform.
• Collaborate with ML engineers to support training, inference, and evaluation.
• Translate complex requirements with sport scientists into durable systems.

Kenntnisse

Full-time software/data engineering

Production data infrastructure

Time-series data

Real-time streaming ingestion

Python

SQL

Go

AWS

Graph data models

Multi-tenant systems

Data provenance/observability

ML/AI data foundations

Kubernetes/ECS

REST/GraphQL

PostgreSQL/MongoDB

Tools

InfluxDB

TimescaleDB

Prometheus

ClickHouse

Kafka

Kinesis

Flink

Spark Streaming

Pulsar

Python

SQL

Go

AWS

ECS

EC2

Lambda

SNS

SQS

GraphQL

REST

gRPC

PostgreSQL

MongoDB

Jobbeschreibung

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 experiencedML/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.

• 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.