Job description:

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.

 
• Start Date: ASAP
• Contract Duration: 6 months (extension likely)
• Location: Remote, with possible occasional in person team sessions / workshops / gatherings (i.e. 1x quarter) likely to take place in Prague
• Languages: English fluent
• 9-6/10-7 CET possibility of a wider overlap (flexibility) appreciated

 
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.
 

Scope:

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

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
  
  

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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