MLOps Engineer

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

Remote / Hybrid — offices and coworks in major Polish cities

Employment type:

B2B

Operating mode:

Remote / Hybrid — offices and coworks in major Polish cities

Location:

Poland

About deepsense.ai

deepsense.ai isa120-person AI/ML consultancy. We’re anOpenAI Advanced Partner and anAnthropic Service Partner, with adirect line toboth labs’ solution engineers. Wealso build production work onElevenLabs, soyou’re getting early, hands-on access tonew models and features before they hit the mainstream. For over adecade we’ve delivered applied AI projects for companies like J&J, Sky, John Deere, and GLS — spanning LLM applications, agents, MLOps, and data science. We’re apeople-first organization that believes indeep technical craft and real ownership. Our engineers don’t just advise; they build things that run inproduction.

About the role

We’re looking for anMLOps Engineer toturn machine learning prototypes into reliable, production-ready systems — and tohelp shape how those systems are built from the start. You’ll work closely with data scientists and engineers todesign pipelines, infrastructure and deployment workflows that meet real requirements around performance, reliability and cost. You’ll build and operate ML systems across cloud environments, from Kubernetes and CI/CD toobservability, versioning and infrastructure ascode, while increasingly supporting LLM-based applications and their unique production needs. It’s ahands-on role across the full path from prototype toproduction, with real influence over architecture and the opportunity towork across different clients, technologies and engineering cultures.

Responsibilities

• Build and operate ML pipelines inproduction cloud environments, including Kubernetes-based deployments
• Work with data scientists from the design stage, somodels meet functional, performance and cost requirements rather than being retrofitted later
• Own CI/CD for ML code, models and infrastructure, and maintain infrastructure ascode
• Design and introduce versioning for code, models, datasets and prompts
• Build and maintain the observability layer for production ML: metrics, logs, traces, dashboards, alerting, drift and performance tracking, cost tracking
• Set upserving and observability for LLM components, including inference endpoints, retrieval pipelines, evaluation harnesses, latency and token budgets, and tracing ofprompts and responses
• Keep infrastructure reproducible and documented sothat solutions can behanded over between teams and clients
• Act asthe MLOps point ofcontact inclient projects: explain technical tradeoffs tomixed technical and business audiences, agree onrequirements, and help client teams adopt what you build
• Share practices across our teams through code review, internal standards and mentoring ofless experienced engineers

You must have

• Hands-on experience with atleast one major public cloud and its ML and data services. GCP and Azure are the most common inour current projects; solid AWS ormulti-cloud experience isequally welcome
• Docker and Kubernetes used inproduction
• Practical experience with monitoring and observability stacks. This can beGrafana with Prometheus, Loki, Tempo and Mimir, the cloud-native equivalents, orcomparable tooling, including instrumentation with OpenTelemetry
• Experience with ML-specific tracking and registry tooling such asMLflow, Weights and Biases oranequivalent, and with LLM observability tooling such asLangfuse, LangSmith orsimilar
• Strong Python and SQL, and comfort with both relational and NoSQL data stores
• Git, and experience working with various branching strategies such asGitFlow, trunk-based development orGitHub Flow
• CI/CD and infrastructure ascode indaily use, for example Terraform, Jenkins, GitHub Actions orGitLab CI
• Enough machine learning, including deep learning, tounderstand what makes ML systems different from ordinary software: training and serving skew, data drift, reproducibility, experiment tracking and evaluation
• Ability towork directly with data scientists and turn research code into maintainable production components
• ADevOps mindset: ownership ofwhat you deploy, abias towards automation over manual steps, and interest inreliability, security and cost rather than only ingetting something torun once
• Willingness towork with different clients, codebases and toolchains, including adapting tostandards set bysomeone else
• Clear written and spoken communication inEnglish and Polish, and the ability todocument decisions sothat others can pick them up

You may have

• Ray for distributed training, tuning orserving, and experience with distributed compute frameworks ingeneral, for example Spark orDask
• GitOps tooling for Kubernetes such asArgo CD orFlux, and related tooling such asHelm and Kustomize
• Previous MLOps orDevOps experience supporting ML project teams, ideally across several clients orproducts
• Production experience with LLM serving stacks such asvLLM, with vector databases, guardrails, and regression testing ofprompts and outputs
• Aninformed view onthe tradeoffs between cloud providers’ ML services
• GPU workloads: scheduling, utilization and cost control
• Feature stores and workflow orchestrators such asAirflow, Dagster, Kubeflow orVertex AI Pipelines
• Experience inpre-sales orsolution scoping conversations, for example estimating effort orproposing anarchitecture before aproject starts

WeOffer

• Work onfrontier AI problems with real clients and real production stakes
• Close collaboration with Anthropic and OpenAI through our partnerships, working atthe frontier ofapplied AI
• Ateam of120+ specialists you can learn from and co-create with
• Competitive compensation benchmarked tothe market
• Flexible hybrid work — remote+offices and coworks inmajor Polish cities
• Conference budget and learning allowance
• Equipment ofyour choice

Impactful AI projects

• Tackle industry-grade challenges: from LLMs for drug discovery toGenAI onedge devices, AI voicebots, and open-source initiatives with global reach.
• Collaborate directly with our partners for early access totools before public release, testing inproduction, and bringing know-how from AI leaders into our projects.
• Contribute toopen-source initiatives like ragbits (1.6k+ stars onGitHub), adopted and appreciated bythe ML community.

Growth & Knowledge Sharing

• Join AI specialists who share expertise through Tech Talks, workshops, and internal trainings.
• Present your work atconferences, run experiments, and stay ahead ofthe curve.
• Choose your own career path and get support for your development.

Flexibility & Culture

• Work fully remote, from one ofour two offices (Warsaw, Bydgoszcz), orfrom coworking spaces inPoznań, Łódź, Wrocław, and Gdańsk.
• Enjoy flexible working hours.
• Benefit from aculture that prevents burnout and supports balance indaily work.
• Start with onboarding – from day one you are matched with abuddy.
• Access apremium AI development suite: OpenAI ChatGPT, Claude, Gemini Advanced, GitHub Copilot, Cursor AI IDE, Claude Code, NotebookLM, plus the latest emerging AI tools tosupport your daily work.
• Work inagile teams with fast decision-making and space totry out your ideas.

Basic Benefits

• Take part inteam-building activities and holiday celebrations.
• Get private medical care and aMultisport card.
• Use our company library, attend onsite English lessons, and benefit from adedicated training budget.
• Join free team lunches and enjoy fresh fruit & snacks.