
Smart Systems Developer
3 days ago
The AI Engineer plays a pivotal role in designing and implementing intelligent systems that leverage agentic AI capabilities for autonomous decision-making and task execution. This involves integrating advanced machine learning models with reasoning, planning, and interaction capabilities.
To excel in this position, one should possess strong proficiency in Python, particularly in scalable and clean code architecture, as well as microservices (e.g., FastAPI, Flask, asyncio). A solid understanding of API integration patterns and inter-service communication (e.g., REST, Kafka) is also essential.
Experience with authentication and authorization mechanisms (e.g., OAuth2, JWT, Azure AD) is highly valued. Additionally, having delivered at least two ML/AI solutions to production, ideally involving document understanding, NLP or search/retrieval systems, is a significant asset.
Practical knowledge and hands-on experience with RAG architectures, LLMs (e.g., OpenAI, Antropic), vector databases (e.g., FAISS, Azure AI Search), embeddings (e.g., OpenAI), and strong grasp of NLP techniques are crucial. The ability to evaluate trade-offs and select appropriate ML/AI techniques for a given problem is also vital.
Able to develop PoC's and iterate quickly based on results. Familiarity with LangChain, LlamaIndex, or similar agentic frameworks is highly desirable. Strong debugging, profiling, and optimization skills for AI applications are necessary.
Key Responsibilities:
- Design and implement intelligent systems using agentic AI
- Integrate advanced machine learning models with reasoning, planning, and interaction capabilities
- Develop and deploy scalable and clean code architecture
- Implement authentication and authorization mechanisms
- Deliver ML/AI solutions to production
Requirements:
- Python
- LLM
- Prompt engineering
- Vector databases
- RAG architecture
- Agentic libraries
- Computer Vision
- Document Processing
Tools: .
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