TL;DR

Senior Machine Learning Engineer (AI): Designing, developing, and maintaining end-to-end machine learning solutions from data ingestion to production deployment with an accent on building scalable ML infrastructure and data pipelines. Focus on applying best practices in MLOps, leveraging cloud platforms and IaC tools, and exploring advanced techniques including LLMs and embeddings.

Location: Remote from Germany

Company

%hirify_global% is a global technology company with approximately 2000 professionals across four continents, driving innovation and growth within various industries for leading brands.

What you will do

Design, develop, and maintain end-to-end machine learning solutions, covering data ingestion, feature engineering, training, evaluation, and production deployment.
Build scalable ML infrastructure and data pipelines for real-time and batch inference.
Collaborate with product managers, data scientists, and software engineers to integrate ML models into customer-facing applications.
Apply MLOps best practices to ensure reliability, reproducibility, and monitoring of ML systems.
Leverage cloud platforms (primarily AWS) and IaC tools (e.g., Terraform) for ML infrastructure deployment and management.
Explore and apply advanced techniques, including LLMs, embeddings, and recommendation systems.
Mentor team members and contribute to improving engineering and ML practices.

Requirements

Work from Germany
Proven experience across the entire ML lifecycle: data pipelines, model training, and serving.
Strong programming skills in Python or Golang.
Hands-on experience with ML infrastructure components (feature stores, model registries, vector databases like Weaviate, Pinecone).
Experience with Model inference in real-time and batch settings.
Knowledge of Cloud platforms (especially AWS) and Infrastructure as code tools like Terraform.
Knowledge of ML platforms (SageMaker), Orchestration frameworks (e.g., Airflow), and Big data systems (e.g., Databricks).

Nice to have

Familiarity with LLMs, embedding-based retrieval, and recommendation systems.
Comfortable collaborating with cross-functional teams.

Culture & Benefits

Performance management with regular discussions (twice a year) for career path shaping.
Training and mentorship programs, including a skill matrix, specialized courses, and a buddy program.
Access to tech hubs for internal exchange and the latest technologies.
Promotion of connections across teams and hierarchical levels, with regular team and on-site events.
Numerous additional benefits: public transportation, childcare allowance, capital-forming benefits, EGYM Wellpass, Job Bike.