ML Platform Engineer

Work Model:Hybrid – 3 days in office

Contract Type:Permanent

Responsibilities

• Design, develop, test, and maintain AI/ML infrastructure within a scalable microservices architecture running on Kubernetes.
• Build and maintain high-quality, secure, and reliable Dev Ops 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

• 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, Tensor Flow, ONNX)
• Experience building clean, maintainable, and testable systems following modern software engineering principles
• Knowledge of cloud-native development and Dev Ops 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