Senior Computer Vision, MLOps Engineer

Protex AI

. Partner with product managers to scope, design and drive implementation .

Posted 10/9/2026full-timeBudapest • HungarySeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in machine learning and computer vision, with a strong focus on building and deploying optimized models, driving the machine learning lifecycle, and ensuring high-quality standards in testing and reproducibility. Proven ability to lead technical projects, mentor engineers, and communicate effectively with cross-functional teams.

Highest-signal resume keywords

Machine Learning EngineeringComputer VisionDeep Learning (PyTorch, TensorRT, ONNX)Cloud Infrastructure (AWS)MLOps

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonModel EvaluationData PipelinesTraining PipelinesDeployment PipelinesCode ReviewArchitectural DesignDebuggingTesting StandardsReproducibility

Soft Skills

Clear CommunicationMentoringProduct Sense

Tools & Technologies

AWSContainersInfrastructure as Code

Industry Keywords

Machine Learning LifecycleComputer Vision StrategyProduction MonitoringRoot-Cause AnalysisTechnical Direction

Tech Stack

Tools & technologies

AWSCloudPythonPyTorch

About the role

Key responsibilities & impact

Partner with product managers to scope, design and drive implementation
Set the technical direction for the ML platform
Balance computer vision trends with real-world compute, storage and cost constraints
Lead triage and root-cause analysis when detection quality drops at client sites
Build components, tools and processes that prevent production issues and accelerate detection and resolution
Review code as the quality gate
Define standards for testing and reproducibility
Mentor and unblock engineers through pairing, reviews and enablement sessions
Act as the technical voice of computer vision with product, infrastructure, application development, installation, technical support and external partners
Contribute to computer vision strategy
Drive the machine learning lifecycle, including data and training pipelines, model evaluation and release, and production monitoring

Requirements

What you’ll need

BSc in Computer Science, Engineering or other relevant STEM
5+ years of software or ML engineering experience, including hands-on work on computer vision or machine learning systems in production
Hands-on experience with deep learning for computer vision, from training to deploying optimised models (e.g. PyTorch, TensorRT, ONNX)
Strong Python skills
Experience building data, training or deployment pipelines on cloud infrastructure (AWS preferred), with containers and infrastructure as code
Solid grasp of model evaluation and MLOps
Track record of owning architectural components, leading architecture discussions and driving complex technical projects across teams from design to production, without formal authority
Experience debugging production issues end to end
Experience driving best practices in testing, reproducibility and code review, and mentoring other engineers
Product sense and clear communication; ability to connect model quality to client outcomes, write clear design docs, and translate strategic goals into actionable engineering plans

Benefits

Comp & perks

Hybrid work arrangement – 2 days/week in-office
Full-time direct hire employment
Inclusive and equal opportunities workplace
Equitable workplace regardless of gender, civil status, family status, sexual orientation, religion, age, disability, education level, or race