AI Software Engineer

IOMETE is the sovereign data platform for the AI era. Enterprises run our data lakehouse, built on Apache Spark and Apache Iceberg, in their own trusted environment: on-premises, in their own cloud, or hybrid. It is built for regulated industries such as financial services, healthcare and government. Customer data never leaves the customer's trust perimeter, and the AI we build on it stays inside that perimeter too.

We are seeking a highly skilled and motivated AI Software Engineer to join our team. We tackle a variety of exciting challenges, from connecting AI tools and models to the lakehouse to diagnosing Spark applications and troubleshooting failures. You'll build agents that run inside the customer's environment, on LLMs the customer provides or, later, on custom models we train for Spark tuning and troubleshooting. You'll also build the framework that defines those agents and the harness that runs, limits, traces and evaluates them.

We are building the AI platform for the lakehouse — Lakehouse Intelligence. The team's roadmap includes, but is not limited to, the MCP Server for AI Agents, which lets AI tools query the lakehouse; the Model Gateway, through which IOMETE's AI features call models; agents for Spark application diagnosis, troubleshooting and onboarding; and SQL AI Functions, which run language models from inside a query. Down the road, you'll also get to fine-tune and build custom models for our platform's specific needs, small enough to run inside a customer's deployment.

• Contribute to and maintain IOMETE's AI services, including the MCP Server for AI Agents, the Model Gateway, SQL AI Functions, and lakehouse-specific agents (such as Spark Application Diagnosis) and their harness.

• Build AI enablers: extend the IOMETE lakehouse and platform to support more AI-powered features, for example by making Spark job logs, query history and table metadata available to them.

• Be hands-on with Kubernetes, including the Helm charts that ship our AI services into each customer's deployment.

• Collaborate closely with external customer engineering teams to support integrations, troubleshoot complex issues and provide guidance for successful adoption of our platform.

• Participate in design discussions about agents, model access, evaluation, and overall systems architecture.

• Write the tests, docs and release notes for what you build. A feature is done when it has shipped with all of them.

• Review your teammates' work and provide feedback.

• 5+ years of professional experience in software engineering (we’re open to exceptional candidates with less experience who demonstrate deep systems knowledge and impact).

• Deep understanding of LLM-based systems in production – tool calling, structured output, retrieval, evaluation, and how they fail.

• Our AI features are written mostly in Python and Go. The data platform is built on the JVM, in Kotlin and Java, and new platform features are being built in Go. Production experience with any of these languages is a big plus, but we know strong engineers learn a new language fast.

• Familiarity with distributed systems and their challenges (e.g., data consistency, fault tolerance, scheduling).

• Fluency in English, both written and verbal — this role involves working with international customers and partners.

• Based in a time zone that overlaps Amsterdam working hours (CET/CEST).

• Hands-on experience across the AI stack: agents and their evaluation, tool protocols such as MCP, model routing and serving, and fine-tuning.

• Prior exposure to data platforms like Snowflake, Databricks, etc.

• Experience with Apache Spark (especially tuning Spark jobs), Apache Iceberg, or contributing to open-source compute engines.

• Hands-on experience with Kubernetes and Helm.

• Exciting projects and challenges.

• Opportunity to work with cutting-edge technology.

• Ability to play a key role in shaping the direction, culture, and architecture of a young & growing engineering organization.

• Competitive compensation and stock options.

1. Introduction call — 30 mins max

2. Technical conversation (with a challenge) — 90 mins

3. Team interview — 60 mins

4. Offer