Senior MLOps Platform Engineer

Nearmap; Warszawa; On-site; PLN 180,000 - 320,000; Full time

Benefits offered by this job

Medical care

Sport Card

MultiLife

Job summary

Nearmap is expanding its machine learning operations team to scale end-to-end workflows for large data sets and distributed architectures. The role emphasizes building robust MLOps pipelines, observability, and collaboration with data scientists to deploy LLM and AI workflows.

The candidate will mentor junior engineers, enhance production reliability, and contribute to architectural decisions that drive scalable, maintainable systems across cloud environments.

Qualifications

• 5+ years of professional experience in MLOps, DevOps, or Software Engineering.

Responsibilities

• Execute software engineering tasks to support end-to-end ML operations, focusing on scaling workflows for large data and distributed systems.
• Design, build, and maintain MLOps systems, including microservices, queuing systems, APIs, and orchestration workflows using Python, Kubernetes, Kafka, and modern databases.
• Implement observability tools such as Prometheus and Grafana to ensure reliability, performance, and visibility of ML systems in production.
• Collaborate with data scientists and ML engineers to streamline workflows for LLM, generative AI, and Agentic AI development and deployment.
• Review architecture and implementation plans to ensure alignment with organizational goals, scalability, and best practices.
• Mentor junior and mid-level engineers, fostering a culture of collaboration, innovation, and operational excellence

Skills

MLOps

DevOps

Distributed systems

CI/CD

Test-driven development

Education

Bachelor’s or Master’s degree in CS/Engineering

Tools

Python

Linux

Kubernetes

Kafka

Prometheus

Grafana

OpenTelemetry

Terraform

AWS

GCP

Job description

Nearmap is expanding its machine learning operations team to scale end-to-end workflows for large data sets and distributed architectures. The role emphasizes building robust MLOps pipelines, observability, and collaboration with data scientists to deploy LLM and AI workflows.

The candidate will mentor junior engineers, enhance production reliability, and contribute to architectural decisions that drive scalable, maintainable systems across cloud environments.