ID: 13072 Location: Budapest Job type: Machine Learning Engineer

Our client is a global technology company. Due to expansion, they are building an IT team in their Budapest office. We are looking for a new colleague for the position of Machine Learning Engineer to join this team.

Responsibilities:

Design and architect the AI/ML models platform to support scalable, efficient, and high-performance machine learning workflows.
Build and manage infrastructure that supports the deployment of machine learning models. This includes leveraging cloud services (AWS), CDK, and containerization tools like Docker.
Architecting and developing MLOps systems with tools such as AWS Sagemaker, MLFlow, Stepfunctions, Lambdas.
Lead the design and implementation of CI/CD pipelines to automate model deployment and rollback processes, ensuring that models can be delivered seamlessly to production aiming to reduce manual intervention and increasing system reliability.
Ensure scalability and efficiency of the models to handle real-time predictions and batch processing.
Set up monitoring and logging solutions for tracking the performance of models in production (DataDog, Cloudwatch).
Define and promote best practices in MLOps.
Provide technical leadership and mentorship to MLOps engineers on technologies, and standard processes.
Partner with the global engineering team to drive cross-functional alignment and ensure seamless integration of AI ML models into wider data ecosystem.
Work closely with Data Scientists, DevOps teams, and Product Managers to ensure that machine learning models are integrated into business workflows and deployed effectively.
Stay up-to-date with the latest trends and technologies in MLOps and machine learning deployment and identify opportunities to incorporate new tools or practices to improve efficiency.

Qualifications and Skills:

5+ years of experience in MLOps or related roles, with at least 2+ years in a senior engineering capacity
Proven experience leading and mentoring teams, managing multiple stakeholders, and delivering projects on time
Proficiency in Python is essential
Experience with shell scripting, system diagnostic and automation tooling
Proficiency and professional experience of ML and computer vision
Have built and deployed ML, computer vision or GenAI solutions (PyTorch, TensorFlow)
Experience working with databases to manage the flow of data through the machine learning lifecycle
Experience with cloud-native services for machine learning, such as AWS SageMaker, MLFlow, Stepfunctions, Lambdas is essential
Deep expertise in Docker for containerization of machine learning models and tools is essential
Experience delivering environment using infrastructure-as-code techniques (AWS CDK, CloudFormation)
Experience setting up and managing continuous CI/CD pipelines for ML workflows using tools like Jenkins, GitLab
Experience in fast-paced, innovative, Agile SDLC
Strong problem solving, organization and analytical skills
Experience with Databricks is beneficial
Experience in building and managing training, evaluation and testing datasets in beneficial
Knowledge of security best practices in the context of machine learning.