Forward Deployed AI Engineer

RevoData is a boutique Data & AI consultancy and proud Databricks Gold partner. We focus exclusively on Databricks, allowing us to go deep and do things properly. We help companies across Europe unlock the value in their data by designing and building scalable platforms that support real business use cases.

We foster a no-ego, no-nonsense culture where collaboration, proactivity, and knowledge sharing are part of everyday work. If you enjoy solving practical problems with strong technical depth and working closely with clients to create lasting impact, you will find plenty of room to do meaningful work here.

As a Forward Deployed AI Engineer, you will work closely with clients to turn AI capabilities into robust, production-ready solutions that address real business problems. This role sits at the intersection of hands-on engineering, technical leadership, and consulting, with scope to shape how solutions are designed, delivered, and improved over time.

Responsibilities

• Embed with clients to scope, design, build, deploy, and improve production-ready AI solutions that address real business problems.
• Translate ambiguous business challenges into technical roadmaps and lead projects from experimentation through delivery, monitoring, and measurable evaluation.
• Build scalable training and inference pipelines, production-grade GenAI applications, and automated MLOps and LLMOps workflows.
• Advise technical stakeholders, explain architecture decisions, and collaborate hands-on with client engineers.
• Work extensively with the Databricks Data Intelligence Platform, including PySpark, SQL, Delta Lake, Unity Catalog, MLflow, and Mosaic AI.

You are a builder who thrives as much in the first as in the last mile of AI - the space between a cool demo and a robust, production-ready system. You enjoy turning AI capabilities into real-world applications that people actually use, and you don't just ship a working prototype; you can prove it works, then make it measurably better.

We'd love to talk if:

• You've built AI systems that run in the real world: You have experience across the full lifecycle - from early experimentation to deployment and monitoring - and understand what it takes to make AI systems reliable in production.
• You're experienced with LLMs & GenAI: You've moved beyond simple prompts and have built production-grade GenAI applications, including RAG architectures, agentic workflows, and fine-tuning, using platforms like OpenAI, Anthropic, Azure AI, or similar.
• You're comfortable with GenAI frameworks: You've used tools like LangChain, LangGraph, Pydantic AI, Hugging Face, or DSPy, and understand how to structure and orchestrate AI-driven applications beyond simple prototypes.
• You're evaluation-driven: You don't trust "it seems better." You establish a baseline, define metrics that actually capture the failure modes that matter, and iterate against them - so you can show with numbers that a change to a prompt, retrieval step, model, or parameter genuinely moved the needle.
• You understand LLMOps & MLOps: You understand that AI is only as good as its lifecycle management. You're comfortable with experiment tracking, model versioning, observability, and deployment (e.g. MLflow or comparable tooling), and you know how to build automated pipelines that handle monitoring, evaluation, and CI/CD for both traditional ML and LLMs.
• You can work with data and build scalable pipelines: You've worked with data in real environments and understand how to design training and inference pipelines that scale with usage and complexity - and work reliably in production.
• You're a pragmatic Python Engineer: You have extensive experience with Python and its data/ML libraries, and you write clean, testable, well-structured code. You favour focused, self-contained solutions over sprawling ones, and you know what not to build under real-world time and budget constraints.
• You're comfortable working in the cloud: You've deployed or worked with AI solutions in environments such as AWS, Azure or GCP, and understand the basics of building and running systems there.
• You have a Consultant Mindset: You enjoy being client-facing. You don't just execute; you advise. You can translate a vague business challenge into a technical roadmap and lead a project from scoping to delivery.
• You are eligible to work in the Netherlands: You can legally work in the Netherlands.

Nice to haves:

• Experience with Databricks or a relevant Databricks certification, for example ML Engineer or GenAI Engineer
• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Experience with production LLMOps tooling, including drift detection
• Dutch proficiency

• Competitive salary: Competitive annual salary bands for Medior, Senior, and Principal levels: €50,000–€65,000, €65,000–€90,000, and €90,000–€110,000.
• Tech Playground: Love experimenting? So do we. Play around with Databricks AI/BI, Genie, Agent Bricks, Vector Search, Terraform, Terragrunt, Asset Bundles, CI/CD, monitoring & alerting tools, Cursor, and more.
• Unlimited Learning: Take advantage of our partner academies, weekly workshops, and lunch & learns.
• Fun Clubs & Activities: Join our climbing club, padel club, kickboxing sessions, Dutch club, or start your own!
• Work-Life Balance & Prime Location: We trust you to get the job done — whether you're an early bird or a night owl. We work in a flexible hybrid setup with Wednesdays together at our Amsterdam office, while the rest of the week is a blend of working from home and client office visits (typically 0–2 days), depending on client preferences.
• Wellbeing & Mobility: €75 net monthly wellbeing budget to spend however you like (gym memberships, bouldering gear, Calm app subscriptions, etc.), plus gym access and e-bike access.
• Allowances: Monthly work-from-home, travel, and internet/phone allowances.
• Additional Perks: Pension plan, regular community events, quarterly team outings, and workation options!