Asks for

The ad says“Durable execution experience. You have worked with durable, fault tolerant execution: retries, failover, durable state, and human in the loop. You understand the failure modes, not just the happy path.”
The ad says“Serverless and function platforms at scale. Knative, KEDA, OpenFaaS, Fission, or similar. You know scale to zero, warm pools, cold start tradeoffs, and per request isolation.”
The ad says“Comfortable in Python and with infrastructure as code (for example Terraform) in a cloud native stack.”
The ad says“Serverless and function platforms at scale. Knative, KEDA, OpenFaaS, Fission, or similar. You know scale to zero, warm pools, cold start tradeoffs, and per request isolation.”
The ad says“Serverless and function platforms at scale. Knative, KEDA, OpenFaaS, Fission, or similar. You know scale to zero, warm pools, cold start tradeoffs, and per request isolation.”
The ad says“You have built and operated systems on Kubernetes in production, and you are comfortable working from the ground up. This role is not about owning one slice of an existing system, or only operating infrastructure someone else designed. It is product work: building something new and owning the outcome.”
The ad says“Serverless and function platforms at scale. Knative, KEDA, OpenFaaS, Fission, or similar. You know scale to zero, warm pools, cold start tradeoffs, and per request isolation.”
The ad says“Comfortable in Python and with infrastructure as code (for example Terraform) in a cloud native stack.”
The ad says“Serverless and function platforms at scale. Knative, KEDA, OpenFaaS, Fission, or similar. You know scale to zero, warm pools, cold start tradeoffs, and per request isolation.”
The ad says“Comfortable in Python and with infrastructure as code (for example Terraform) in a cloud native stack.”
The ad says“Streaming and long running workloads. Server sent events or websockets, long lived connections, and the operational reality of keeping them alive through deploys and node churn. Agentic or AI workloads are a plus.”

TL;DR

We are hiring a Product Engineering Tech Lead to own the layer that runs our customers' AI applications: serverless, durable, streaming execution, and the observability that proves it works. You sit where backend engineering, platform operations, and product meet. You scale this platform to enterprise grade, push it into new territory like long running autonomous agents, lead the team that builds it, and own the result end to end. This is a builder role with a founder mindset, not a maintainer role.

Why deepset

At deepset, we’re on a mission to make custom AI solutions accessible to every organization. With Haystack, thousands of developers build advanced LLM applications every day, while our enterprise AI Platform helps companies turn large language models into business value. We’re remote-first, flexible, and built on a culture of trust and ownership. You’ll collaborate with top-tier tech talent, tackle meaningful challenges, and help transform complex AI into solutions that are simple, powerful, and ready for the real world.

What you will do

Own the serverless execution platform. Pipelines and agents need to run fast, reliably, and concurrently at enterprise scale, and they need to behave identically whether invoked serverless or running as a deployed service. You'll architect the execution layer that makes that true, including the durability work (surviving node evictions, spot terminations, rolling deploys) needed to keep long-running agents alive for hours, not seconds.
Own streaming and long-lived connections. Conversational and agentic workloads here are long-lived, not simple request/responses. You'll own how those connections behave under pressure: backpressure, graceful shutdown, reconnects, for both end users and API clients.
Lead the team and the architecture. Set technical direction, make the hard tradeoffs, and translate product strategy into a system that holds up under enterprise load. You stay hands-on on the problems that matter most, even as your day-to-day coding share shifts over time.
Make reliability provable. Own observability and metering: tracing, alerting, per-customer cost and usage visibility, so the platform's reliability isn't just a claim, it's something you can show a customer. You'll also own on-call and incident response, closing the loop between what you build and what you operate.

What you bring to the team

Builder and founder mindset. You have started things, shipped them, and lived with the consequences. You think about what is next, not only about fixing what is in front of you. Startup or founder experience is a strong signal here.
You have built and operated systems on Kubernetes in production, and you are comfortable working from the ground up. This role is not about owning one slice of an existing system, or only operating infrastructure someone else designed. It is product work: building something new and owning the outcome.
Durable execution experience. You have worked with durable, fault tolerant execution: retries, failover, durable state, and human in the loop. You understand the failure modes, not just the happy path.
Serverless and function platforms at scale. Knative, KEDA, OpenFaaS, Fission, or similar. You know scale to zero, warm pools, cold start tradeoffs, and per request isolation.
Streaming and long running workloads. Server sent events or websockets, long lived connections, and the operational reality of keeping them alive through deploys and node churn. Agentic or AI workloads are a plus.
Operator instinct. Observability, alerting, service levels, and incident response are second nature. You hold the platform to a production grade standard because it has to work reliably for real customers.
You can lead a team and connect strategy to robust architecture. You make the technical direction legible to the rest of the team and to the business.
Comfortable in Python and with infrastructure as code (for example Terraform) in a cloud native stack.

Nice to have

Built Kubernetes operators or controllers, or automated deployments as a system rather than a script.
Taken a platform from zero to one all the way to production grade ownership.
Open source contribution or maintenance background.
Exposure to B2B SaaS, LLM, or agent infrastructure.

Benefits

Remote-first setup with flexible hours & tech of your choice
30 days vacation + extra days for family sick leave
Competitive salary & stock options for every team member
Monthly sports & mental health support allowance with Oliva
Annual learning & development budget
Monthly team socials & in-person meetups
Dog-friendly Berlin HQ

About us

Founded in 2018, deepset builds open and enterprise-grade tools that help teams build AI with purpose. From Haystack, our open-source framework, to the Haystack Enterprise Platform, we give developers and organizations the building blocks to solve complex, high impact challenges with AI with full control, transparency, and sovereignty. Backed by GV and Balderton, we’re growing the world’s production AI community and customer base solving challenges too critical to get wrong.