AI Platform Engineer

Qualifyze

2d • Hybrid

About The Position

Qualifyze is hiring an AI Engineer to join its Engineering team and bring AI solutions from prototype to production. The role focuses on building the systems, infrastructure, and deployment processes behind LLM-based and agentic applications. The engineer will own AI solutions across their lifecycle, from architecture and development through deployment, monitoring, and cost optimisation, and contribute to technical practices supporting AI development across the organisation.

Requirements

• At least 4 years of experience in software engineering, ML engineering, or MLOps/platform engineering, including at least 2 years building and deploying LLM-based systems in production.
• Strong Python skills and experience developing production APIs and backend services.
• Practical experience with LLM solutions such as OpenAI, Anthropic, or Mistral, and techniques such as RAG, embeddings, vector databases, and agent orchestration.
• Experience with cloud platforms such as AWS or GCP and their AI services, such as Bedrock, SageMaker, or Vertex AI.
• Practical experience with Docker, Kubernetes or equivalent technologies, CI/CD, and Infrastructure as Code such as Terraform.
• Experience monitoring and operating AI or ML systems in production.
• Ability to communicate technical decisions clearly to engineering teams and business stakeholders.
• A structured and pragmatic approach to problem-solving, with a sense of ownership.

Nice To Haves

• Experience in regulated industries such as pharma, life sciences, healthcare, or fintech.
• Familiarity with LLM evaluation tools and LLMOps platforms such as Langfuse or MLflow.
• Knowledge of AI governance, regulatory frameworks, and IT security standards such as the EU AI Act, ISO 42001, and ISO 27001.
• Experience developing internal tools or supporting developer enablement initiatives.
• Experience with agentic coding.

Responsibilities

• Design, develop, and deploy production-ready LLM applications, including RAG pipelines, agents, and workflow automation.
• Evaluate and select appropriate models, frameworks, and providers based on each use case.
• Develop evaluation processes and benchmarks to measure AI quality, reliability, and potential regressions before and after release.
• Design and manage cloud infrastructure for AI workloads using AWS, Azure, or GCP and Infrastructure as Code.
• Build and maintain CI/CD pipelines for models, prompts, and AI services, including automated testing, controlled releases, and rollback processes.
• Deploy and scale AI services using containers and orchestration tools such as Docker, Kubernetes, or equivalent managed solutions.
• Implement monitoring and observability for AI systems, including tracing, latency, output quality, drift, and potential failure points.
• Establish usage controls and optimise costs through model selection, caching, and batching.
• Investigate and resolve production issues across the AI stack.
• Keep up to date with AI developments and identify techniques and tools that can bring practical value.

Benefits

• Competitive compensation package, reviewed annually.
• Country-specific vacation package that increases with tenure.
• Country-specific benefits, including private health care, fitness, and meal allowances.
• Annual personal development budget.
• Hybrid working mode, plus the option to work from anywhere for up to 3 months of the year.
• Equipment and tools to work effectively.
• Annual in-person all-company event and celebration in Europe.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed