Project Description:
Internal AI-driven platform designed to automate and standardize repetitive data processing tasks. The system focuses on use cases such as product categorization, context-aware translation, and structured data enrichment.

One of the core components, enables users to configure classification pipelines and upload domain-specific knowledge files. Based on this input, the system processes structured datasets (e.g., Excel files with multiple attributes like category, subcategory, size) and assigns accurate values to each field.

For example, for a given product the system determines the correct classification (e.g., Shampoo instead of Laundry Detergent) using contextual reasoning.

The processing pipeline runs on Databricks (DBR), where dynamic prompt generation and LLM-based inference are used to enrich and classify data at scale. The platform integrates backend services, AI pipelines, and cloud infrastructure to deliver reliable and reusable AI capabilities across business units.

Job Description:
Key Responsibilities:
1. AI Integration: Connect foundational models (via APIs or open-source hosting) to web applications using clean code.
2. System Optimization: Monitor latency, manage token usage, handle model caching, and implement evaluation frameworks to minimize hallucinations.
3. Team Collaboration: Partner with AI engineers and product teams to translate AI use cases into stable product features.
Required Technical Skills
1. Languages: Strong proficiency in Python (FastAPI, Pydantic)
2. AI Orchestration: Hands-on experience with frameworks like LangChain, LlamaIndex, or LangGraph.
3. Data & Infrastructure: Competency in SQL, Docker, Git workflows, and cloud AI suites (AWS, GCP Vertex AI, or Azure AI).
4. AI Standards: Understanding of prompt engineering, evaluations (e.g., Ragas), and tool-connection protocols like MCP (Model Context Protocol).
5. Preferable knowledge of AI Factory (PyrogAI IPA/IPA0)

17 openings across locations