Machine Learning Engineer

We're looking for a seasoned Machine Learning Engineer with a strong background in event-driven architecture, real-time systems, and AI/LLM integration. You'll be responsible for architecting and implementing high-performance, distributed systems with dynamic functionality, real-time processing, and seamless AI model interaction. This role is perfect for someone passionate about deep Python internals and building intelligent infrastructure at scale.

Responsibilities:

• Design and develop scalable, distributed systems using Python.
• Build and maintain LLM/AI integration pipelines (OpenAI, Azure OpenAI, open-source models).
• Develop real-time communication systems (text/voice over WebSockets).
• Create dynamic function registration and runtime execution frameworks.
• Implement robust error handling, monitoring, and logging systems.
• Write clean, modular, and well-documented code following best practices.
• Collaborate with cross-functional teams on system architecture and performance tuning.

Requirements:

• 5+ years of professional Python development experience.
• Strong problem-solving and debugging skills.
• Excellent written and verbal communication.
• Self-starter with close attention to detail.
• Comfortable reviewing code, writing documentation, and mentoring peers.
• Ability to understand and manage complex system interactions.

Expertise in:

• Advanced Python: decorators, metaclasses, async/await, etc.
• Asynchronous programming with asyncio.
• Event-driven architecture and WebSocket communication.
• Dynamic function registration and runtime execution.
• Dependency injection and context management patterns.
• Type hints, Pydantic, and runtime type enforcement.

Deep understanding of:

• OpenAI / Azure OpenAI API integration.
• Real-time communication protocols.
• API design (REST, GraphQL optional).
• Error handling in distributed systems.
• Logging and monitoring (e. g., Sentry, custom metrics).
• Testing strategies (unit, integration, mocking async flows).

Nice to Have:

• Experience with:
• Deploying open-source LLMs on Azure (e. g., HuggingFace Transformers, Llama.cpp, etc. ).
• FastAPI, GraphQL, or similar modern Python frameworks.
• Docker, Kubernetes, and containerized deployments.
• CI/CD pipelines and release automation.
• Sentry, Prometheus, or other observability platforms.