ML Platform Engineer | Berlin OR Hessen | Germany

Acquism SARL; Berlin; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Acquism SARL in Berlin seeks an experienced AI/ML platform engineer to design, implement, test, and maintain scalable infrastructure on Kubernetes. You will work across the backend stack, integrating Kafka, gRPC, and REST services while building robust data pipelines with Spark.

Lead ML lifecycle management with MLflow, contribute to scalable architecture, and ensure secure, compliant deployments in production environments, including air-gapped setups with CPU/GPU constraints.

Qualifikationen

• Degree in Computer Science, Engineering, or equivalent practical experience.
• 5+ years of AI/ML platform engineering or related roles.
• Strong experience with Kubernetes, distributed systems, and data engineering technologies.
• Hands-on experience with ML platforms and frameworks (MLflow, PyTorch, SparkML).
• Familiarity with modern data stack technologies (Spark, Delta Lake, TensorFlow, ONNX).
• Experience building clean, maintainable, and testable systems following modern software engineering principles.
• Knowledge of cloud-native development and DevOps practices (including Helm).
• Experience in security-sensitive or highly regulated environments is a plus.
• Strong problem-solving and debugging skills.
• Excellent communication skills in English and collaboration across teams.

Aufgaben

• Design, develop, test, and maintain AI/ML infrastructure in a scalable microservices architecture on Kubernetes.
• Build and maintain DevOps pipelines and Helm charts.
• Integrate event-driven systems (Kafka), gRPC services, and REST APIs.
• Develop and optimize data pipelines using Spark and related tools.
• Manage ML lifecycle processes with MLflow and related platforms.
• Contribute to architecture decisions for scalability, performance, and reliability.
• Support deployment and monitoring of ML models in production, including air-gapped setups.
• Ensure platform security and compliance aligned with ISO 27001.

Kenntnisse

Kubernetes

Distributed systems

Data engineering

Ausbildung

Degree in Computer Science or Engineering

5+ years AI/ML platform engineering experience

Tools

MLflow

PyTorch

SparkML

Spark

Delta Lake

TensorFlow

ONNX

Jobbeschreibung

• Design, develop, test, and maintain AI/ML infrastructure within a scalable microservices architecture running on Kubernetes.
  
• Build and maintain high-quality, secure, and reliable DevOps pipelines and Helm charts
  
• Work across the backend stack, integrating event-driven systems (Kafka), gRPC services, and REST APIs
  
• Develop and optimize data pipelines using modern data engineering tools (e.g., Spark)
  
• Manage ML lifecycle processes using tools such as MLflow
  
• Contribute to architectural decisions to improve scalability, performance, and system reliability
  
• Support deployment and monitoring of ML models in complex production environments, including isolated (air-gapped) setups with varying hardware constraints (CPU/GPU).
  
• Ensure platform reliability and robustness in customer-deployed Kubernetes environments
  
• Maintain high security and compliance standards aligned with industry best practices (e.g., ISO 27001)
  

Requirements

• Degree in Computer Science, Engineering, or equivalent practical experience
  
• 5+ years of experience in AI/ML platform engineering or related roles
  
• Strong experience with Kubernetes, distributed systems, and data engineering technologies
  
• Hands-on experience with ML platforms and frameworks (e.g., MLflow, PyTorch, SparkML)
  
• Familiarity with modern data stack technologies (e.g., Spark, Delta Lake, TensorFlow, ONNX)
  
• Experience building clean, maintainable, and testable systems following modern software engineering principles
  
• Knowledge of cloud-native development and DevOps practices (including Helm)
  
• Experience working in security-sensitive or highly regulated environments is a plus.
  
• Strong problem-solving and debugging skills
  
• Excellent communication skills in English and ability to collaborate across teams