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