Our stack

Python 3.12 and Go as our core languages - Python for rapid development, Go for performance-critical services and internal tools.

Cloud-native infrastructure: Kubernetes orchestration with Helm, multi-cloud deployment across AWS.

GCP, and AzuregRPC, REST APIs, WebSockets and Kafka for service communication.

GitHub Actions for CI/CD, Docker for containerization, with Grafana, Prometheus, and OpenTelemetry for observability.

PostgreSQL, ClickHouse for analytics, and specialized vector databases for AI workloads.

Responsibilities

Architect high-performance real-time systems: Design micro-services and streaming architectures that handle millions of concurrent voice/text AI interactions with ultra-low latency.

Create foundational platform components: Build reusable infrastructure blocks (rate limiters, service mesh, circuit breakers), developer tools (APIs, SDKs), and observability solutions that enable teams to ship AI features rapidly.

Optimize AI infrastructure end-to-end: Manage GPU clusters, design model serving pipelines, implement vector similarity search, and collaborate with ML engineers to ensure efficient model deployment and inference.

Enable engineering excellence: Establish platform standards, deployment patterns, and self-service automation that empower product teams to move fast while maintaining reliability.

Requirements

Pragmatic engineering approach: Balance speed and quality by shipping working solutions quickly when needed, while keeping code clear and maintainable over clever abstractions.

Strong programming skills in Python (expert level with type systems and modern ecosystem) and Go (for performance-critical components).

Production-scale distributed systems experience: Building and operating micro-services architectures with service discovery, API gateways, event-driven patterns, and proven ability to handle large-scale production workloads.

Real-time systems and observability expertise: Experience with streaming architectures, message queues, low-latency optimization, plus deep knowledge of distributed tracing, metrics aggregation, and log analysis at scale.

Strategic thinking: For critical architectural decisions, you invest time to evaluate multiple approaches, present top candidates with trade-offs, and document why alternatives were rejected.

Outstanding technical communication: Ability to clearly explain complex architectural decisions, write comprehensive documentation, facilitate productive technical discussions, and effectively communicate with both engineering teams and stakeholders.

What we offer?

High-impact projects with prominent clients and our proprietary AI platform development.

Cutting-edge technology stack with the latest tools in AI infrastructure and platform engineering.

Budgets for the latest AI models (GPT-4, Claude, etc.) to ensure our team has the best tools available.

Rapid career progression working alongside senior professionals from leading tech companies.

Remote work flexibility from Europe.

Direct influence on platform architecture decisions that shape our AI products.