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

Property intelligence is reshaping how the world understands the built environment, and Nearmap is driving that. We put powerful aerial imagery, AI-driven analytics, and geospatial tools into the hands of the people who plan, build, insure, and govern the places we all live and work. Our technology turns property uncertainty into decisive action, and our culture brings out the best in the people who build it.

We move fast, we care about craft, and we're proud of what we're building. If you're energized by turning hard problems into real-world impact, we'd love to meet you.

Job Description

• Execute software engineering tasks to support end-to-end machine learning operations, with a focus 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 database systems.
• Implement observability tools such as Prometheus and Grafana to ensure reliability, performance, and visibility of ML systems in production
• Collaborate closely with data scientists and machine learning 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

Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• 5+ years of professional experience in MLOps, DevOps, or Software Engineering, with a focus on building scalable and reliable software systems.

Core Technical Expertise

• Proficiency in Python and Linux, with strong knowledge of designing scalable, distributed systems.
• Hands-on experience in designing, implementing, and maintaining MLOps workflows, including CI/CD pipelines, monitoring, and production optimization.
• Strong background in cloud computing (AWS/GCP), infrastructure as code (Terraform), containerization, and orchestration (Kubernetes).
• Solid understanding of modern software development practices such as test-driven development (TDD), systems thinking, and CI/CD automation.

Observability & Reliability

• Experience deploying and managing observability tools such as Prometheus, Grafana, and OpenTelemetry to ensure high reliability and performance in production ML systems.
• Expertise in scaling and optimizing distributed systems for large-scale, multi-node computations.

What we offer:

• Medical care
• Sport Card (MultiSport)
• MultiLife (mental and physical wellbeing)