Senior AI Engineer (all genders)

About the roleheyData is building a compliance platform that guides companies — from 10-person startups to 400-person multi-entity organisations — step by step to audit-ready outcomes across ISO 27001, NIS2, GDPR, and more. Compliance is fundamentally a knowledge and process problem, and AI is one of the most powerful tools we have to solve it.We're hiring our first Senior AI Engineer: the person who will identify where AI can eliminate manual work, build the systems that make it happen, and own those systems end-to-end. You'll work directly with Product, Engineering, and our compliance experts to discover real problems and ship real solutions.This is a builder role. You'll spend most of your time shipping AI-powered features that customers and internal teams can actually use — so you need to make infrastructure decisions that are pragmatic today and extensible tomorrow.The role is fully remote, with a focus on Germany and Serbia.What you'll doDiscover and define AI opportunitiesWork directly with Product, the VP of Engineering, the Engineering Team Lead, and compliance experts to understand pain points and manual workflows that AI can automate.Translate fuzzy problem descriptions into clear AI feature specs: what the system does, what data it needs, how success is measured.Push back confidently when AI is not the right solution — and propose alternatives.Build and ship AI-powered featuresDesign and implement AI features using foundation models (OpenAI, Anthropic, Mistral) as a starting point, customised with RAG pipelines, prompt engineering, and fine-tuning where justified.Expose your AI services as clean RESTful APIs consumed by the rest of the application (NestJS/VueJS stack on AWS EKS).Build data ingestion and transformation pipelines to feed AI systems with heyData's internal data and customer data, handled with strict GDPR compliance.Integrate AI agents and multi-step workflows where appropriate, using frameworks like LangChain or LlamaIndex pragmatically.Own the AI lifecycleManage the full lifecycle of every AI system you build: development, deployment, monitoring, and retraining.Set up CI/CD pipelines for AI models integrated into our existing AWS infrastructure.Monitor model quality in production — detect drift, evaluate output quality, and trigger retraining when needed.Document your systems clearly so the team can understand, operate, and extend them.Experiment and measureDesign and run AI/ML experiments rigorously: clear hypotheses, appropriate evaluation metrics, statistically sound interpretation.Communicate findings to non-technical stakeholders in plain language. Help the team make decisions based on evidence, not intuition.Stay current with the rapidly evolving AI landscape (model providers, tooling, evaluation frameworks) and bring relevant developments to the team.What we're looking for5+ years total software engineering experience, with at least 3 years specifically building and operating production AI/ML systems.Proven track record shipping LLM-powered features to real users.Hands-on experience with major foundation model APIs: OpenAI, Anthropic Claude, Mistral. Familiarity with platform abstractions like AWS Bedrock or Google Vertex AI.Production experience with RAG architectures: vector databases (Pinecone, Weaviate, pgvector), embedding model selection, chunking strategies, retrieval evaluation.Solid Python engineering: production-grade APIs and services. Comfortable building services that integrate cleanly into a microservices architecture.Experience with CI/CD for AI systems: model versioning, experiment tracking (MLflow, Weights & Biases or similar), monitoring, and automated retraining pipelines.SkillsStrong problem decomposition: you can take a vague pain point and design an AI system that actually solves it.Statistical thinking: you design experiments properly, interpret results rigorously, and know when findings are meaningful.Clear communication: you can explain model trade-offs, architecture decisions, and experiment results to engineers, product managers, and non-technical stakeholders alike.Data pipeline fluency: you can build data ingestion and transformation infrastructure, and you understand the difference between data that's useful and data that's noisy.Mentality & valuesTrustworthy: you ship what you commit to, and you communicate early when something changes.Ownership-driven: you own your systems from first prototype to production monitoring. "Not my job" isn't in your vocabulary.Curious: you actively explore problem spaces with stakeholders. You ask questions until you understand the real problem, not just the stated one.Pragmatic: you reach for the simplest solution that works. You don't fine-tune when RAG is enough, and you don't build infrastructure you don't yet need.Collaborative: you work well with engineers who aren't AI specialists, and with domain experts who aren't technical. You bring people along.Why heyDataYour growth matters:High ownership, direct imp