MLOps Engineer — Remote/Hybrid, Production ML Systems

deepsense.ai; Poland; Hybrid; PLN 180,000 - 260,000; Full time

Benefits offered by this job

Flexible hybrid work

Conference budget

Learning allowance

Equipment of your choice

Job summary

deepsense.ai is seeking an experienced MLOps Engineer to turn machine learning prototypes into robust, production-ready systems. You will design pipelines, infrastructure, and deployment workflows across cloud environments, collaborating with data scientists and engineers to meet performance, reliability and cost requirements.

You will own CI/CD for ML code, models and infrastructure, implement versioning for data assets, and build observability for production ML, including metrics and tracing.

Qualifications

• Hands-on experience with at least one major public cloud (GCP/AWS/Azure).
• Docker and Kubernetes in production environments.
• Experience with monitoring/observability stacks (Grafana/Prometheus, Loki, Tempo, Mimir) or equivalents.
• Experience with ML tracking tools (MLflow, Weights & Biases) and LLM observability tools (Langfuse, LangSmith).
• Strong Python and SQL, with both relational and NoSQL data stores.
• Git and branching strategies (GitFlow, trunk-based, GitHub Flow).
• CI/CD and infrastructure as code in daily use (Terraform, Jenkins, GitHub Actions, GitLab CI).
• Understanding ML-specific issues like drift, reproducibility, experiment tracking and evaluation.
• Ability to turn research code into production components with data scientists.
• DevOps mindset: automation, reliability, security and cost awareness.

Responsibilities

• Build and operate ML pipelines in production cloud environments.
• Collaborate with data scientists to meet performance and cost requirements.
• Own CI/CD for ML code, models and infrastructure.
• Design and introduce versioning for code, models, datasets and prompts.
• Maintain the observability layer for production ML workloads.
• Set up serving and observability for LLM components, including endpoints and pipelines.
• Keep infrastructure reproducible and well documented for handovers between teams and clients.
• Act as MLOps point of contact in client projects and explain tradeoffs to mixed audiences.
• Share practices across teams through code reviews and mentoring.

Skills

Public cloud

Docker

Kubernetes

Monitoring & observability

ML tracking

LLM observability

Python

SQL

Git

CI/CD

Infrastructure as code

ML/AI fundamentals

English & Polish

Tools

Grafana

Prometheus

Loki

Tempo

Mimir

Terraform

Jenkins

GitHub Actions

GitLab CI

Job description

deepsense.ai is seeking an experienced MLOps Engineer to turn machine learning prototypes into robust, production-ready systems. You will design pipelines, infrastructure, and deployment workflows across cloud environments, collaborating with data scientists and engineers to meet performance, reliability and cost requirements.

You will own CI/CD for ML code, models and infrastructure, implement versioning for data assets, and build observability for production ML, including metrics and tracing.