Senior Software Engineer - Integrations - AI/ML

United States Digital Space LLC; Netherlands; On-site; EUR 90,000 - 130,000; Full time

Job summary

United States Digital Space LLC is seeking a Senior Software Engineer to own and evolve critical AI/ML ecosystem integrations. You will work at the intersection of high-performance database engineering and developer experience, crafting tools for Engineers and Data Scientists to harness speed and scale in frameworks they already use.

The role emphasizes production-grade Python connectors, vector stores, embedding pipelines, and retrieval-augmented generation, with ownership of LangChain,

Qualifications

• 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer.
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities

Responsibilities

• Own and evolve the company's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Drive the AI/LLM integration strategy: designing connectors and patterns that make the company a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

Skills

Python development

Data Scientist / ML Engineer

Pandas

NumPy

Pydantic

SQL

Async programming

Tools

LangChain

LlamaIndex

n8n

Vector stores

Embedding pipelines

LLM tooling

Job description

The Connectors team is the bridge between the company and the broader ecosystem. We build and maintain the integrations that make the company accessible to millions of developers, data practitioners, and AI agents worldwide, from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms that ingest millions of events per second, observability systems that monitor global infrastructure, and, increasingly, AI-powered data applications that redefine how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure the company integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of the company's AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness the company's speed and scale in the frameworks they already use.

We're looking for someone who has firsthand experience as an AI & ML Engineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You’ll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with the company as a vector store, ML feature stores, and LLM-powered data applications.

the company’s columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real: building the robust, production-ready connectors that make the company the natural choice when data practitioners design their next-generation AI and data systems.

What you’ll do

• Own and evolve the company's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Drive the AI/LLM integration strategy: designing connectors and patterns that make the company a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities

Bonus points for

• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with the company or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem
• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Familiarity with tools like dbt, Airflow, Dagster, Prefect is a plus