Machine Learning Engineer

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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.
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