Ml Engineer / Tech Lead @ eConsulting
(práce v Polsku)

Ml Engineer / Tech Lead @ eConsulting

Polsko

Must Have:

• Kubernetes (K8s) – Deep, production-level expertise is non-negotiable. 
• Able to design and deploy clusters from scratch, manage massive workloads (10,000+ nodes). 
• Cloud Experience (AWS / GCP) – Strong hands-on experience managing infrastructure in cloud environments. 
• SRE / DevOps Background – Solid understanding of reliability engineering, monitoring, on-call operations, and CI/CD. 
• Programming Skills (Python preferred) – Strong coding ability; Go or Rust experts open to learning Python are also welcome. 

Nice-to-Have:

• Experience with MLOps platforms and lifecycle management. 
• Familiarity with workflow orchestration tools (e.g., Airflow, Kubeflow). 

Workplace: Hybrid model (2 days from the office per week), Warszawa/Łódź

Workload: Full-time

Contract type: Contract of Mandate / B2B

Role Overview

We’re looking for a Principal Machine Learning Systems Engineer (Tech Lead) to join a global leader in geospatial analytics. 

In this role, you’ll become the technical owner and architect of Machine Learning workflow orchestration platform – a mission-critical ecosystem that underpins the entire AI division. You’ll design, build, and maintain scalable infrastructure capable of supporting massive data and compute workloads, while leading a 5-person engineering team in Poland.

This position is ideal for a hands-on technical leader who thrives at the intersection of systems design, scalability, and team leadership. You’ll spend roughly 30–40% of your time coding, while setting technical direction and mentoring your team.

What we offer:

• Access to LinkedIn Learning 
• B2B contract + benefits 

,[Act as the technical architect and owner of the ML orchestration platform powering all AI workflows. , Design and build large-scale distributed systems running on Kubernetes (up to 10,000 nodes). , Ensure platform reliability, scalability, and high availability in production environments. , Lead and mentor a team of 5 engineers (2 senior + 2 mid-level), fostering technical excellence and autonomy. , Collaborate closely with AI, SRE, and Data Engineering teams to streamline model training and deployment pipelines. , Define and drive best practices in CI/CD, observability, and cloud infrastructure management. , Stay hands-on with coding and architecture reviews (~30–40% time in technical delivery). , Take ownership of incident response, on-call rotations, and reliability improvements. ] Requirements: Machine Learning, AI, Kubernetes, Cloud, DevOps, CI/CD, coding, MLOps, Airflow, Kubeflow, AWS, GCP, Python

Kategorie

artificialIntelligence

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