johannesboyne

DQC.ai | Senior Software Engineer | 65-95k€ + VSOP

https://dqc.ai/careers/senior-software-engineer-backend

We're building the AI-powered platform for enterprise data. Automatically analyse data quality, automatically fix and enhance data, and prevent bad data. Our platform automatically detects issues in master and transactional data, generates improvement suggestions using AI and human expertise, and prevents problems at the source — across systems like Snowflake, Databricks, SAP, and more.

*The problem we're solving*: Bad data costs businesses millions. Rules get written manually, issues get caught too late, and fixes are slow. We're replacing that with agentic AI that generates data quality rules, finds anomalies, and drives corrections (!) yes, it automatically fixes the broken data — with humans in the loop where it matters. Our customers across industrial goods, energy, automotive, and healthcare are already seeing real results.

*About us*: DQC is a Munich-based AI startup founded in 2022. We're a small (15 ppl), fast-moving team where engineers have real ownership and direct impact on product direction and technical architecture. Short decision paths, no bureaucracy, steep learning curves.

*The role*: As a Senior Backend Engineer, you'll own the full lifecycle of feature development — from architecture and design to production deployment. You'll work cross-functionally with backend, frontend, and product to ship features that thousands of users rely on. Our platform sits at the intersection of backend systems, modern data stacks, and LLM-driven workflows, so you'll be building at the frontier of what's possible with AI in production. We practice agentic coding daily and our tech interview includes a live agentic-coding exercise — we want engineers who embrace these tools with judgment, not hype.

If you want real ownership, direct product impact, and the chance to build at the intersection of backend engineering and AI — let's talk.

A very similar "how to guide" can be found here https://ampcode.com/how-to-build-an-agent written by Thorsten Ball. In general Amp is quite interesting - obviously no hidden gem anymore ;-) but great to see more tooling around agentic coding being published. Also, because similar agentic-approaches will be part of (certain/many?) software suits in the future.

The combination is truly key, how we do it:

- Combining LLMs, gradient boosting, and multiple statistical/heuristic based approaches - For LLMs: rely on (quite) large context length + fine-tuning - The (ML) models are used to extract mathematically provable quality checks

reg. 1) Which one has been used?

We (https://dqc.ai) are doing something in the space, yes. Next to a mixture of ML, and heuristic based approaches, plus link & integrations into source systems. Happy to talk about it, feel free to reach out.

One super interesting startup in this sector: https://www.labtwin.com/ they completely automated lab reporting and made it voice-first

Hehe... hi Lars, long time no "see" ;-) Yes, he is and I was also very happy to see the project getting mentioned here! I used it myself and was always impressed about it!

Atfarm - https://at.farm - uses satellite data to provide precision fertilization maps for farmers across the globe. (Disclaimer: involvement in the setup)

For all the German fellows: I'll give a talk about "exactly" this topic including a hands-on sample at the "Monster on Rails" Meetup this week in Münster - http://www.meetup.com/Monster-on-Rails-Web-Development-Meetu...

+1 for simplicity

And if it is faster than boot2docker with VirtualBox, great!

Has one of you tried xhyve? https://coreos.com/blog/coreos-and-xhyve-tech-preview/

Thanks rcarmo for bringing this up. You're right, it is a better, and the only right, approach to declare the dependencies like you proposed. I've updated the code!

Yes, docker-compose (https://docs.docker.com/compose/) is just great. Another sweet tool, but I haven't used it in production yet, is empire (https://github.com/remind101/empire) for deployment (AWS centric).

https://www.youtube.com/watch?v=Ll50EFquwSo Building and scaling container driven cloud infrastructure - Bryan Cantrill (Video about Triton)

This Blog Post https://www.joyent.com/blog/understanding-triton-containers explains it very well!

yep

It is a difficult question, and actually not quite clear in what context you want to gain speed. Is it the pure coding or is it the development of solutions? I'll go for the later ;)

Structure your work and try to develop a strategy. So think about (w)hat, (w)hy, ho(w), (w)here, (w)hen. - What: What is the core problem to solve? - Why: If it is a 'problem', why is it a problem, is there an existing solution? If not, why is the feature important - empathize with the feature request. - How: What is your strategy, how do you want to conquer this task? - Where: Where does the solution has to be implemented to fit best? - When: Programming is a process (a timely and logical sequence of activities which are required), thus define what step has to be done before the next. Example: one should not start with the development of a "user scores database" if the main objective is to build a jump-and-run game.

These steps are helping myself to be more productive.