Senior Software Engineer - LLM Ops & Evals

Datasnipper; Amsterdam; On-site; EUR 90,000 - 140,000; Full time

Job summary

DataSnipper is hiring a senior backend/platform engineer to own the LLM gateway, deploy models, and run the shared evaluation platform. You will shape production AI infrastructure used by hundreds of thousands of users, with a focus on reliability, security, and cost attribution.

You will work across Azure and GCP, implement infrastructure as code, and collaborate with product and ML engineers to build self-service platform capabilities that scale with demand.

Qualifications

• 5+ years in backend or platform engineering, with strong production Python
• Experience building or running LLM inference infrastructure: a gateway or routing layer with multiple providers, failover, rate limiting and cost attribution
• Experience with cloud at the infrastructure level and infrastructure as code we run across Azure and GCP with Terraform
• Experience running a shared service in production: on-call, incidents, postmortems, SLOs
• Hands-on experience with observability tools (OpenTelemetry, Grafana), including instrumenting services and designing dashboards and alerts
• Comfort with privacy and compliance work: PII handling, anonymisation, retention, access control
• Experience building platform or shared-service capabilities consumed by multiple internal teams

Responsibilities

• Own the LLM gateway: routing, provider failover, rate limits, retries, and cost attribution across multiple model providers
• Deploy, version and deprecate models across clouds, regions and environments, including quota and capacity planning, managed as infrastructure as code
• Build the shared evaluation platform: versioned datasets, experiment tracking, run and result schemas, reporting, and trace linkage back to the run
• Own the infrastructure for async and long-running AI workloads
• Own observability for AI traffic: latency, retries and fallbacks, token usage, cost and errors, per team and per use case
• Take part in the on-call rotation, run incidents, and close the follow-ups
• Implement the security and compliance controls the platform is held to: retention, access control, RBAC and SSO
• Define and maintain clean integration contracts between the platform and the teams that consume it
• Partner with product and ML engineers to turn their requirements into platform capabilities that are self-service rather than a request queue

Skills

Backend engineering

Python (production)

LLM inference infra

Cloud infrastructure

Infrastructure as code

Observability tooling

On-call & incidents

Privacy & compliance

Tools

Terraform

OpenTelemetry

Grafana

Azure

GCP

Job description

Every AI call in DataSnipper goes through us. Product teams do not talk to model providers directly, they talk to our gateway. We are responsible for how inference is routed, how it fails over, what it costs, and how anyone can tell whether the output is any good.

The second half of the job is evaluation. We are building the platform teams use to measure AI quality: versioned datasets, experiment tracking, and evaluation runs they can act on. It is a hard problem and largely an open one, so you will have real influence over how we solve it.

This is a small team with a large blast radius. You will own real production systems, set the standards other teams build against, and see your work in front of hundreds of thousands of users in audit and finance.

About DataSnipper

Audit and finance are still massively manual and we are changing that. DataSnipper is a $1B, bootstrapped unicorn with 600,000+ users across 180+ countries, already embedded in the daily workflows of top audit and accounting firms.
Now, we are taking things further with our Excel Agent, bringing AI directly into where the work actually happens. Unlike generic AI tools, we do not sit on the sidelines. Our AI operates inside Excel, with access to real documents and audit evidence, meaning it does not just generate answers, it does the work, with full traceability.
We are not just applying AI, we are redefining how audit gets done. If you want to build something category-defining at scale, this is the place.

What you will do

Technical Delivery

• Own the LLM gateway: routing, provider failover, rate limits, retries, and cost attribution across multiple model providers

• Deploy, version and deprecate models across clouds, regions and environments, including quota and capacity planning, managed as infrastructure as code

• Build the shared evaluation platform: versioned datasets, experiment tracking, run and result schemas, reporting, and trace linkage back to the run

• Own the infrastructure for async and long-running AI workloads

Reliability, Security & On-Call

• Own observability for AI traffic: latency, retries and fallbacks, token usage, cost and errors, per team and per use case

• Take part in the on-call rotation, run incidents, and close the follow-ups

• Implement the security and compliance controls the platform is held to: retention, access control, RBAC and SSO

Collaboration & Impact

• Define and maintain clean integration contracts between the platform and the teams that consume it

• Partner with product and ML engineers to turn their requirements into platform capabilities that are self-service rather than a request queue

What you will bring

Must-Have

• 5+ years in backend or platform engineering, with strong production Python

• Experience building or running LLM inference infrastructure: a gateway or routing layer with multiple providers, failover, rate limiting and cost attribution

• Experience with cloud at the infrastructure level and infrastructure as code we run across Azure and GCP with Terraform

• Experience running a shared service in production: on-call, incidents, postmortems, SLOs

• Hands-on experience with observability tools (OpenTelemetry, Grafana), including instrumenting services and designing dashboards and alerts

• Comfort with privacy and compliance work: PII handling, anonymisation, retention, access control

• Experience building platform or shared-service capabilities consumed by multiple internal teams

Nice-to-Have

• Experience with LLM or agent evaluation

• Temporal or another durable workflow engine

• Self-hosted inference, capacity planning, load testing

• Synthetic data or document anonymisation pipelines

• Document AI: VLMs, OCR, structured extraction and the metrics that go with it

• Domain experience in audit, accounting or fintech

What we expect

• Ownership: You own work end-to-end, anticipate issues, and ensure high-quality delivery without close supervision

• Growth Mindset: You encourage open feedback exchange and provide clear, balanced feedback that helps others grow

• Collaboration: You build strong cross-functional relationships and influence peers through expertise, data, and empathy

• Adaptability: You navigate ambiguity calmly, model positive behavior, and help peers adjust through clear communication

• Judgment: You exercise sound judgment in ambiguous situations, balance speed and accuracy, and adjust priorities proactively

Recruitment steps

• Recruiter screen

• Hiring Manager interview

• Peer programming session

• System design interview

• Final interviews with Engineering leadership