We are looking for a professional [Seniors / Leaders to help us build scalable, robust and production-grade ML platforms for our clients. In this role you will work alongside our MLOps and machine learning engineers to build infrastructure for training and serving ML models (including LLMs) and supporting the full lifecycle of AI development, through automation, monitoring and the best MLOps practices.
responsibilities :
design and implementation of modern, scalable ML infrastructure (cloud-native or on-premise) to enable both day-to-day work of teams as well as deployment of pipelines and models,
collaboration with Data Scientists and Machine Learning Engineers to design the architecture of MLOps solutions to meet functional and performance requirements,
implementing and ensuring compliance with the best MLOps practices in the areas of automation (e.g. CI/CD), monitoring, versioning (code, data, models) and infrastructure,
performing code reviews for other MLOps Engineers but also other roles in the team,
delivering high quality code and infrastructure, properly tested and aligned with the project requirements.
requirements-expected :
proficiency in the MLOps domain, awareness of best practices, frameworks, tools,
strong experience with cloud (AWS, GCP, Azure), ability to design cloud-native applications using dedicated cloud services for: serverless functions, batch processing, managed kubernetes, relational databases, object storage, data warehouses, message buses, streaming and serverless ML platforms,
experience with deployment of applications in Kubernetes environments (using tools like Helm) but also provisioning, administration and troubleshooting of existing Kubernetes clusters,
experience with Terraform or other Infrastructure-as-a-Code solutions (e.g. Pulumi),
hands-on experience in (object-oriented) programming in Python in particular in AI/ML related use cases, such as development of ML pipelines or model serving,
expertise in enhancing ML systems with proper automation (CI/CD, GitOps, GitHub Actions, ArgoCD), monitoring (CloudWatch, Prometheus, Evidently) and versioning (Git, DVC) tools,
solid understanding of machine learning (including deep learning and LLMs), software engineering and DevOps,
good understanding of Linux systems, essential for maintaining development and production environments.
experience with distributed computing frameworks such as Ray,
experience with design and implementation of data engineering solutions with tools such as: Kinesis, Glue, Airflow, dbt, Great Expectations etc.,
understanding data warehousing (e.g. Snowflake) and data streaming frameworks (e.g., Apache Kafka, Spark, SQL),
experience working with (non-)relational and vector databases (e.g. Pinecone).
offered :
opportunity to work on cutting-edge AI projects with a diverse range of clients and industries, driving solutions from development to production,
collaborative and supportive work environment, where you can grow and learn from a team of talented professionals,
an opportunity to participate in conferences and workshops around the world,
an opportunity to participate in Tech Talks (internal training and seminar sessions),
flexible working hours and remote work options.
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of foreign language classes
sharing the costs of professional training & courses
remote work opportunities
flexible working time
fruits
integration events
no dress code
video games at work
coffee / tea
drinks
sharing the costs of tickets to the movies, theater
