Strong proficiency in Python with production-level coding experience
Solid experience with AI/ML frameworks (PyTorch / TensorFlow)
Good understanding of LLMs, prompt engineering, and real-world AI use cases
Hands-on experience with RAG (Retrieval-Augmented Generation), embeddings, and vector databases (e.g., Pinecone, Weaviate, FAISS)
Experience in designing and implementing AI pipelines (data retrieval reasoning output)
API integration experience with LLM providers (OpenAI, Claude, etc.)
Understanding of multi-agent systems or AI orchestration concepts
Experience with tools like LangChain / LlamaIndex or similar frameworks
Strong knowledge of data handling, preprocessing, and optimization
System & Architecture understanding:
Ability to design scalable AI systems
Understanding of model performance, latency, and cost optimization
Familiarity with microservices and backend integration
Experience with Knowledge Graphs / graph-based reasoning
Exposure to MLOps (deployment, monitoring, model versioning)
Cloud experience (AWS / GCP / Azure)
Experience working on AI products (not just experiments)
We prioritize candidates who have worked on end-to-end AI applications, not just model training.
