ML Platform Engineer 18254
Veritaz ABPublicerad: 2026-06-19
Ansök senast: 2026-07-19
Beskrivning
Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in ensuring the perfect talent fit. With a proven track record of successful partnerships with top companies, we have rapidly grown our presence in the USA, Europe, and Sweden as a dependable and trusted resource within the IT industry.
Assignment Description
We are looking for a ML Platform Engineer for a short-term assignment
What You Will Work On
Design, develop, and optimize machine learning models and pipelines
Build and maintain end-to-end ML workflows, including data preparation, training, evaluation, and deployment
Improve encoder-based models through increased model capacity and higher input resolution
Develop multi-class classification solutions for complex attribute prediction tasks
Build and optimize data pipelines supporting machine learning workflows
Train, evaluate, and monitor machine learning models in production environments
Develop ranking and confidence models based on existing AI outputs
Implement curriculum learning and automated training-data scheduling strategies
Optimize model inference performance and portability across platforms
Develop tooling for model serving, monitoring, and result visualization
Collaborate with software engineers and researchers on AI platform development
Contribute to scalable and maintainable ML infrastructure
What You Bring
Strong Python development and Machine Learning engineering experience
Experience across the full ML lifecycle, including data pipelines, model training, evaluation, deployment, and monitoring
Experience working with encoder architectures, autoregressive models, or self-supervised learning approaches
Knowledge of modern machine learning frameworks and model development practices
Experience building scalable and production-ready AI solutions
Strong understanding of data processing, feature engineering, and model evaluation
Experience developing and optimizing ML inference workflows
Ability to work across both backend systems and lightweight frontend applications
Strong analytical, problem-solving, and communication skills
Experience collaborating in multidisciplinary engineering environments
Assignment Description
We are looking for a ML Platform Engineer for a short-term assignment
What You Will Work On
Design, develop, and optimize machine learning models and pipelines
Build and maintain end-to-end ML workflows, including data preparation, training, evaluation, and deployment
Improve encoder-based models through increased model capacity and higher input resolution
Develop multi-class classification solutions for complex attribute prediction tasks
Build and optimize data pipelines supporting machine learning workflows
Train, evaluate, and monitor machine learning models in production environments
Develop ranking and confidence models based on existing AI outputs
Implement curriculum learning and automated training-data scheduling strategies
Optimize model inference performance and portability across platforms
Develop tooling for model serving, monitoring, and result visualization
Collaborate with software engineers and researchers on AI platform development
Contribute to scalable and maintainable ML infrastructure
What You Bring
Strong Python development and Machine Learning engineering experience
Experience across the full ML lifecycle, including data pipelines, model training, evaluation, deployment, and monitoring
Experience working with encoder architectures, autoregressive models, or self-supervised learning approaches
Knowledge of modern machine learning frameworks and model development practices
Experience building scalable and production-ready AI solutions
Strong understanding of data processing, feature engineering, and model evaluation
Experience developing and optimizing ML inference workflows
Ability to work across both backend systems and lightweight frontend applications
Strong analytical, problem-solving, and communication skills
Experience collaborating in multidisciplinary engineering environments
