Senior AI Engineer m/f/d

Senior AI Engineer m/f/d Overview

This position offers a hands-on engineering opportunity focused on integrating AI-powered capabilities into real-world products and production workflows. As a Senior AI Engineer, you will be responsible for designing, building, deploying, and continuously improving LLM-powered features within a large-scale technology environment. The role requires a combination of strong software engineering skills and practical expertise in areas such as RAG, embeddings, agents, tool calling, and AI orchestration. You will collaborate closely with Product, ML, and Engineering teams to transform complex business challenges into scalable AI solutions.

Key Responsibilities

• Architect, develop, deploy, and maintain production-grade LLM capabilities integrated into customer-facing products and internal workflows.
• Build backend services, APIs, integrations, and supporting infrastructure that connect AI capabilities with product functionality.
• Contribute to full-stack and user-facing development where required, helping deliver complete AI-powered product experiences.
• Implement production-ready patterns involving LLMs, RAG, embeddings, vector search, agents, and tool calling.
• Design reusable components, abstractions, and engineering patterns that improve development velocity, consistency, and maintainability.
• Establish and improve evaluation frameworks, observability, monitoring, and feedback loops for AI-powered features.
• Monitor and optimize AI systems for quality, latency, reliability, scalability, and inference cost.
• Investigate production issues, analyze telemetry, and continuously improve AI functionality based on system performance and user feedback.
• Collaborate closely with Product, ML, and Engineering stakeholders to translate product challenges into practical AI solutions.
• Rapidly prototype and validate new ideas, then transform successful experiments into robust and maintainable production systems.
• Contribute to engineering decisions around architecture, trade-offs, scalability, and long-term technical sustainability.

Requirements

• Demonstrated hands-on experience building and successfully shipping LLM-powered features or applications into production.
• Practical experience with technologies and patterns such as RAG, embeddings, vector search, agentic systems, LLM APIs, orchestration, and tool calling.
• Strong understanding of production LLM considerations, including evaluation, observability, failure modes, latency, reliability, and cost management.
• Experience improving AI features based on telemetry, evaluation results, production behavior, or user feedback.
• Strong software engineering background with experience designing APIs, backend services, and production-grade distributed systems.
• Full-stack development experience with technologies such as Angular, JavaScript/TypeScript, Python, Node.js, or comparable technologies is a strong advantage.
• Ability to write clean, maintainable, well-structured code and take ownership of solutions from implementation through production.
• Willingness and ability to contribute across backend services, AI infrastructure, and user-facing product functionality.
• Experience working with AWS or comparable cloud infrastructure, CI/CD pipelines, and production environments.
• Strong product mindset with a bias toward shipping, rapid iteration, pragmatic problem-solving, and measurable outcomes.
• Comfortable working in an environment characterized by ambiguity, rapid change, and evolving requirements.
• Strong communication and collaboration skills, with the ability to work effectively with Product, ML, and Engineering teams.
• Ability to challenge requirements constructively, communicate technical trade-offs, and translate AI complexity into practical product solutions.
• A maintainability- and reuse-oriented mindset, with a focus on building production systems rather than isolated demonstrations.
• Experience with multi-agent systems, MCP, guardrails, moderation, safety checks, prompt and context management, routing, caching, or fallback strategies is a plus.
• Experience with Docker and cloud platforms such as AWS, GCP, or Azure is beneficial.
• Experience with modern frontend development and customer-facing web applications is an advantage.

Benefits

• Highly visible role with direct access to senior leadership and meaningful influence on product and engineering decisions.
• Strong career growth and continuous learning opportunities in applied AI and software engineering.
• Opportunity to work alongside experienced technology, product, and industry professionals on high-impact AI initiatives.
• International and multicultural working environment with colleagues distributed across multiple countries.
• Flexible work-from-home arrangement.
• $500 home-office setup budget to support a productive remote working environment.
• $1,000 annual learning and development budget to support professional growth and skills development.
• Visa and relocation support where required.
• Opportunity to work on production-scale AI applications and modern LLM technologies.
• Significant ownership and autonomy in taking AI solutions from experimentation through deployment and continuous improvement.
• Competitive compensation aligned with experience, skills, and market standards.

Degree Requirement: Not Specified

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