Senior AI Platform Engineer – ML Infrastructure and RAG Systems

Empower the future of AI by building scalable, innovative ML infrastructure and retrieval systems that shape the way organizations leverage AI capabilities.

Description

As a Senior AI Platform Engineer - ML Infrastructure and RAG Systems, you will develop and maintain internal AI services, APIs, and retrieval systems that support research, engineering, and operational needs. You will help shape AI service delivery and technological innovation at scale.

Your main responsibilities

• Develop and maintain internal AI services and APIs for production environments.
• Build and optimize RAG pipelines, including document ingestion, embeddings, retrieval, and relevance tuning.
• Manage vector database performance, scalability, and data freshness to ensure high retrieval quality.
• Design clear, well-documented APIs to support internal users and workflows.
• Integrate model serving endpoints into application-layer systems, ensuring low latency and high reliability.
• Define and monitor service objectives related to latency, reliability, and retrieval quality.
• Implement prompt management, versioning, evaluation, and testing frameworks for LLMs.
• Build resilient systems with fallback and degradation mechanisms to ensure continuous operation.
• Implement monitoring, tracing, logging, and quality metrics to oversee AI services' health and performance.
• Manage the lifecycle of AI services including deployment, rollout, updates, and deprecation.
• Participate in operational support and incident response to troubleshoot and resolve issues swiftly.

You're ideal for this role if you have

• 4+ years of experience in software or platform engineering, with exposure to AI/ML or LLM applications.
• Strong Kubernetes skills and experience working with containerized environments.
• Good knowledge of AWS, networking fundamentals, IAM, and cloud infrastructure.
• Hands-on experience building and operating production RAG systems.
• Familiarity with vector databases and retrieval systems.
• Strong Python programming skills and experience developing production APIs and services.
• Solid understanding of LLM fundamentals, including prompting, token management, and output reliability.
• Excellent communication skills and the ability to work collaboratively across technical and non-technical teams.

It is a strong plus if you have

• Experience with agentic AI systems and workflow orchestration.
• Knowledge of LLM evaluation frameworks and quality measurement techniques.
• Exposure to model serving platforms and inference optimization.
• Understanding of embedding model trade-offs and retrieval performance tuning.
• Data engineering experience or AI-related data pipelines.
• Relevant AWS or Kubernetes certifications.

Language Required for the role

• Fluent English, with excellent communication skills.

Eligibility for the role

• Only candidates with an existing legal right to work in Europe will be considered for this role.