Software Development Engineer for building scalable AI platforms at Workday. Focus on agentic AI capabilities and collaborate with senior engineers and ML researchers.
About the role
- ML Engineer developing ML Ops infrastructure and AI applications for Uni Systems. Collaborating closely with data scientists and developers in a hybrid work environment.
Responsibilities
- Design, implement and maintain a scalable, reliable and secure hybrid cloud ML Ops infrastructure to deploy, test, manage and monitor ML models in different environments.
- Development and maintenance of software applications in the field of Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL) and/or Artificial Intelligence (AI).
- Work closely with data scientists and back-end developers to build, test, integrate and deploy ML models.
- Analyse performance metrics and troubleshoot issues to ensure high availability and reliability.
- Design CI/CD pipelines, use orchestration solutions and data versioning tools.
- Creating automated anomaly detection systems and constant tracking of its performance and optimising ML pipelines for scalability, efficiency and cost-effectiveness.
- Design the IT architecture for solutions in the NLP / ML / AI fields, and coordinate its implementation considering master- and meta-data management concepts.
- Provision of security studies, security assessments or other security matters associated with information system projects.
- Provision of support and guidance to other team members on MLOps practices.
Requirements
- Master's degree in IT and minimum 15 years of relevant experience (or Bachelor's in IT and minimum 19 years of experience).
- One of the following: University degree in NLP (computer science or computational linguistics), Specialisation in (statistical/neural) machine translation (MT), or University degree in IT / Computer Science / Engineering with specialisation in AI, ML operations or data engineering.
- Strong experience managing on-prem and/or cloud-based MLOps infrastructure.
- Hands-on experience with containerisation and orchestration tools (e.g. Kubernetes, Docker, Podman, EKS, PKS).
- Proficient in MLflow, TensorFlow (TFX), and Airflow.
- Solid experience with AWS and/or Azure.
- Proficient in Python, Unix, and Bash scripting.
- Experienced in handling large-scale, unstructured data and data lakes.
- Familiar with monitoring/logging tools (e.g. ELK, Prometheus, Grafana, OpenTelemetry, CloudWatch).
- Skilled in model testing and validation in production environments.
- Good understanding of Agile development methodologies.
- Experienced with Infrastructure as Code (Terraform, CloudFormation).
- Knowledge of messaging platforms (Kafka, Redis, RabbitMQ).
- Understanding of data security (encryption, ML security a plus).
- Familiar with NoSQL databases (e.g. Elasticsearch, MongoDB, Cassandra, HBase).
- Experience with query languages (SQL, Hive, Pig) and information extraction.
- Excellent English (C-level, written and spoken).
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
Machine Learning / AI Engineering Intern at Teradyne developing AI - driven software systems and collaborating on intelligent automation projects. Focus on applied AI initiatives and real - world engineering challenges.
Senior ML Engineer at BMT designing and deploying machine - learning systems, collaborating across diverse client projects in defence and security sectors.
Machine Learning Engineer responsible for improving MIRA and developing generative AI features at Meltwater. Collaborating with a team to drive innovation and scalability in AI solutions.
AI & ML Engineer enhancing energy management software solutions at GreenPocket GmbH. Focusing on modern LLM architectures and AI integration for innovative user experience.
Machine Learning Engineer responsible for implementing and maintaining data science models in bpx’s machine learning studio. Bridging data science and computational needs to achieve business outcomes.
Machine Learning Engineer at DentalMonitoring developing AI solutions for orthodontics. Responsibilities include model development, evaluation, deployment, and performance monitoring.
Machine Learning Engineer at Hiscox working on fraud detection and generative AI projects. Collaborating closely with data scientists and engineers to solve complex business challenges.
Internship focusing on programming robotic arms and using machine learning in simulations at Fraunhofer IIS. Opportunity to gain practical experience and contribute to innovative research.
Senior Machine Learning Engineer at Itaú, driving innovation with data and AI solutions. Collaborating across teams to implement robust machine learning architectures and ensure scalable deployments.