Responsibilities:
o Turn ambiguous product problems into working AI systems in production
o Evaluate and choose the right approach: embeddings, RAG, fine-tuning, or classical ML
o Build end-to-end pipelines [Data ingestion cleaning feature engineering, Model development
evaluation deployment]
o Develop core capabilities like Semantic search, Product recommendations & intelligent substitutions
o Cost prediction and optimization models
o Partner closely with product and engineering to ship fast and iterate based on real usage
o Continuously improve models using feedback loops and performance monitoring
Required Skills / Qualifications:
o 5 – 8 years of experience in applied AI/ML
o Strong Python skills and hands-on experience with ML frameworks (PyTorch, TensorFlow, or similar)
o Experience working with [LLMs (fine-tuning, RAG, prompt design), Embeddings and vector search]
o Ability to make practical decisions on:
o Model selection [Accuracy vs latency vs cost trade-offs, Experience deploying models into production
environments]
o Comfortable working with messy, real-world data
o Familiarity with tools like LangChain, LlamaIndex, Pinecone, or Weaviate
