AI Software Engineer — Data Lakehouse Intelligence
Key Skills
Job Description
Location: remote, in a time zone that overlaps CET working hours. About us 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. Position overview 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. The AI team 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. What you'll do 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. What we're looking for 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). Preferred qualifications 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. What we offer 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. Hiring process Introduction call — 30 mins max Technical conversation (with a challenge) — 90 mins Team interview — 60 mins Offer
Core Responsibilities
Build and maintain AI services and agents for the IOMETE lakehouse, including the MCP Server, Model Gateway, SQL AI Functions, and tools for Spark diagnosis. Extend the platform to support AI features, deploy services with Kubernetes and Helm, collaborate with customer engineering teams, and deliver tested, documented releases.
Requirements
Candidates should have 5+ years of software engineering experience, strong knowledge of production LLM systems, and familiarity with distributed systems; exceptional candidates with less experience may also be considered. Production experience with Python, Go, Kotlin, or Java is beneficial, while experience with AI agents, MCP, model serving, Spark, Iceberg, Kubernetes, and Helm is preferred; English fluency and overlap with Amsterdam working hours are required.
Benefits
- Stock Options
About IOMETE
Industry: Software Development
Company size: 11-50 employees
IOMETE is the modern data lakehouse for enterprises that need stellar performance without handing control to the hyperscalers. We provide a sovereign, self-hosted lakehouse platform that runs on-premise, in private cloud, or across hybrid environments — ideal for organizations with strict requirements around security, sovereignty, compliance, and cost predictability. Enterprises use IOMETE to replace aging Hadoop and legacy warehouses, consolidate fragmented data stacks, and power analytics, machine learning, and AI workloads at scale. The platform includes high-performance compute (SQL + Spark), ACID-compliant lakehouse storage, governance and lineage, workflow orchestration, ingestion frameworks, and full operational observability — all deployed inside the customer’s own infrastructure. Why enterprises choose IOMETE: • Full control over data, keys, and governance — no SaaS exposure, no Cloud Act risk • Predictable, transparent vCPU-based licensing instead of usage-based cost shocks • No vendor lock-in: open standards, open formats, Kubernetes-native portability • Enterprise-grade security architecture aligned with SOC2/ISO frameworks • Designed for high-security, regulated, and air-gapped environments • Seamless integration with existing systems, including on-prem object storage and Kubernetes • Built for AI: performance for large-scale analytics, ML pipelines, and GPU-enabled workloads IOMETE gives enterprises a modern, cloud-like data platform that runs wherever their data lives — data centers, sovereign cloud, edge, or hybrid deployments. A single platform for analytics and AI, delivered on your terms, with complete control over performance, security, and cost.