When we started in 2014, our focus was on product managers in smaller teams who lacked a great product management tool. Today, we are the leading product management platform, and as we continue to grow, we are now helping more and more enterprise companies with thousands of employees to build products that matter.
We believe Productboard’s greatest differentiator is enabling our customers to collect, understand, and act upon data from the market and empower them to organize them into Insights, understand their customers, and align their company around the most impactful opportunities.
We are in search of an experienced Artificial Inteligence Engineer to become a member of our cross-functional team. Your role will span across the entire AI feature development lifecycle.
You don’t need prior AI experience - just strong Python skills, ideally some cloud/devOps background, and a genuine interest in the AI space.
This role is based in our Brno office with an office-centric hybrid schedule.
AI CAPABILITIES
within our product, and we aim to build more copilot features into Insights and other domains, enabling bulk-processing, pre-processing, smart suggestions and other functionality. In the end, our customers would be presented with aggregated insights & clustered data, which is connected to the features & ideas they plan to build.
OUR CORE CHALLENGES:
- Introducing new capabilities into our solutions – a feature store, eliminating the need of manual processing of incoming Insights data, live predictions, co-usage of different vendor LLMs, etc.
- Ensuring data flows from the frontend via GraphQL – asynchronous processing through Kafka in different services, and back to frontend – produce a seamless UX experience
- Ensuring our core services, which store data from different sources, are ready to scale and meet performance requirements
- Building a robust agentic infrastructure capable of supporting diverse AI-driven use cases, enabling scalable, autonomous decision-making workflows, and ensuring resilience across various customer scenarios
We’re looking for experienced engineering minds, who are able to not only lead big technical projects, but act as knowledge multipliers inside and outside of the team. Those who strive to build top-end services and are able to turn a good system design on paper into a well-tuned working solution.
Our tech stack
You’ll work with the following frameworks, tools, and languages:
- We write our ML code in Python
- When it comes to scheduling ML pipelines, we rely on orchestration frameworks like Celery and Kubernetes
- Our real-time services run on AWS and Kubernetes, backed by Git, CI/CD, Docker, Helm, and Kafka
- For monitoring our services we use Datadog and Sentry, and for a business overview, we’ve got Looker in our toolkit
- Our tech toolbox also includes GraphQL and Postgres, among other technologies
- LangGraph for agent orchestration and workflow management, combined with LangSmith for observability, monitoring, and evaluation of AI agents
About you
We are currently seeking an individual who possesses the following skills and qualities:
- Professional expertise in building Python applications
- Brings a mindset that blends hands-on engineering with architectural thinking—able to simplify complexity by removing rather than adding, inspired by the philosophy of simple Unix tools that do one thing well and compose elegantly together.
- Is highly curious about the AI space—not necessarily an MLOps expert, but someone who actively follows trends and developments like a deeply engaged developer would
- Gets a strong sense of job satisfaction from follow-through and completeness, not just the excitement of getting things off the ground.
You will help us with:
- Building AI-powered product features
- Enhancing and sustaining our internal tech stack, while identifying and incorporating new state-of-the-art technologies
- Discovering and experimenting across different domains, creating MVPs and POCs, engaging in discussions about findings with fellow engineers and the product team, and planning the execution
- Collaborating with other engineers, introducing fresh concepts and methodologies to the team
The team has recently worked on:
- Delivering LLM solutions to our customers
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