We're seeking a senior or AI-savvy mid-level engineer who is highly proficient in async Python/FastAPI and has strong Azure cloud expertise. You should be comfortable building low-latency APIs, optimizing PostgreSQL at scale, and collaborating closely with Data Science and Frontend teams. Fluency with AI coding assistants is essential for accelerated development in our fast-paced environment. Experience with LLM integration is a plus, but willingness to learn and adapt is equally important. You'll work across multiple projects with varying architectures (3 active, 3 upcoming), so flexibility, pragmatic problem-solving, and strong collaboration skills are key to success in this role.

Core Requirements

· Experience3-5+ years of backend development experience

· 2-3+ years of production Python/FastAPI experience

· Azure cloud services experience (REQUIRED): Blob Storage, Azure Kubernetes Service (AKS), AI Search

· Strong async programming patterns and PostgreSQL expertise

· Docker containerization and microservices architecture experience

· Fluency with AI coding assistants (REQUIRED): GitHub Copilot, Cursor, or similar tools for accelerated development

Technical Depth

· Expert-level async/await patterns and non-blocking I/O in Python

· PostgreSQL optimization: complex queries, indexing strategies, connection pooling with asyncpg

· RESTful API design with JWT authentication and CORS configuration

· Database schema management at scale (50+ tables, complex relationships)

· Performance-conscious development (P95 latency optimization, connection pooling strategies)

· Understanding of when to use asyncio vs. multiprocessing for different workload types

Tech Stack

· Core: Python 3.11+, FastAPI, async/await patterns

· Data Layer: PostgreSQL, asyncpg, SQLAlchemy 2.0, Alembic migrations

· Azure (Required): Blob Storage, Azure Kubernetes Service, AI Search, Cosmos DB

· Deployment: Docker, Kubernetes, Helm charts

· AI/ML: LangChain, OpenAI APIs, vector databases

· Supporting Tools: Redis caching, Pydantic v2, pytest

What You'll Do

API Development

· Design and build async FastAPI services with structured logging and low-latency endpoints

· Develop RESTful APIs across multiple microservices (architecture varies by project: 1-6 services)

· Implement WebSocket connections for real-time updates and event-driven patterns

· Optimize database operations with connection pooling (200-pool configurations) and JSONB-aware queries

· Build background task processing systems with retries and idempotency for heavy operations

Infrastructure & Deployment

· Containerize services with Docker and deploy via Helm charts to Azure Kubernetes Service

· Manage environment-driven configuration and execute startup database migrations

· Implement background job scheduling with task schedulers, status tracking, and retry logic

· Optimize caching strategies with Redis for maximum performance

· Configure CORS policies, middleware, and request/response logging

AI/LLM Integration (Nice-to-Have)

· Integrate LangChain and OpenAI APIs for semantic tasks and domain-specific pipelines (valuation, analytics)

· Build document processing systems handling PDF, Excel, and DOCX parsing at scale

· Work with vector databases and Azure AI Search for retrieval-augmented generation (RAG)

· Decouple heavy LLM/document processing from request threads to maintain low P95 latency

· Collaborate with Data Science on prompt engineering, output parsers, and evaluation metrics

Data Architecture

· Design PostgreSQL schemas with proper indexing, foreign keys, and multi-tenant data isolation

· Integrate Azure Blob Storage for document workflows and large file handling

· Implement complex business logic with transactional guarantees and ACID compliance

· Use Cosmos DB connector for specialized NoSQL workloads when appropriate

· Manage database connection lifecycle, pooling, and transaction management

Collaboration & Soft Skills

· Constant collaboration with Data Science teams: Productionize and iterate on LLM pipelines, prompt libraries, and model evaluation

· Close partnership with Frontend engineers: Define API contracts, implement JWT-secured access, orchestrate async jobs via task IDs and polling patterns

· Clear communication on technical decisions, trade-offs, and architectural choices

· Pragmatic problem-solving approach across multiple projects with varying requirements

· Ability to iterate quickly based on feedback from cross-functional teams

· Leverage AI coding assistants to accelerate development while maintaining code quality

Nice-to-Have Skills

· Hands-on Kubernetes/Helm operations experience beyond deployment

· Prior experience with LangChain, LLM integration, or AI/ML pipeline productionization

· Multi-tenant SaaS architecture patterns and data isolation strategies

· Cosmos DB or other NoSQL database experience

· Experience with task schedulers, job queues (Celery, RQ), or workflow orchestration

· Performance profiling and optimization tools (py-spy, cProfile, pgBadger)

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Remote vs Onsite: Remote, with possible occasional in person team sessions / workshops / gatherings (i.e. 1x quarter) likely to take place in Prague

Working overlap needed: 9-6/10-7 CET possibility of a wider overlap (flexibility) appreciated

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