30 days ago
Remote, IndiaSenior
Responsibilities
- Build and own scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python.
- Analyze B2C, product, and customer data to identify trends, performance drivers, and actionable insights.
- Define reliable metrics and decision-support solutions covering retention, conversion, funnels, cohorts, and customer lifetime value.
- Design, build, and deploy LLM-powered applications, agents, analytics workflows, and natural-language data experiences using RAG, tool calling, MCP, and agent frameworks.
- Develop reusable AI tools that help analysts and business users interact with data, automate analysis, and generate insights.
- Evaluate, monitor, and improve production AI solutions for accuracy, reliability, cost, and safety.
- Integrate data platforms, analytics systems, APIs, and AI components into secure, maintainable services.
- Contribute to scalable end-to-end data and AI architecture with platform and engineering teams.
- Build a governed metrics layer, analytics copilot, automated analysis workflows, and reusable AI tooling.
Requirements
- 6+ years of hands-on experience in analytics engineering, data engineering, product or business analytics, applied AI, or a related technical role.
- Strong proficiency in SQL and Python, including production-grade data pipelines and analytical datasets beyond exploratory notebooks.
- Experience designing scalable architectures that integrate data platforms, services, APIs, AI components, and user workflows.
- Experience with B2C, product, or customer analytics and business metrics such as retention, funnels, cohorts, conversion, and customer lifetime value.
- Demonstrated experience personally building and deploying at least one LLM-powered application used by real users through RAG, tool or function calling, or agents.
- Hands-on experience with at least one orchestration or agent framework such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, or OpenAI Agents SDK.
- Ability to explain technical designs and complex concepts clearly to non-technical stakeholders.
- Preferred experience includes AI evaluation, monitoring, guardrails, Responsible AI, conversational analytics, text-to-SQL, semantic layers, governed metrics, vector databases, semantic search, MCP server development, Docker, Kubernetes, and observability tooling.
Tech Stack
Categories
AI ApplicationsData Engineering
About Pearl
Pearl builds AI-powered dental imaging and diagnostic software for dental practices, DSOs, and insurers, using computer vision to detect pathologies on radiographs and support clinical performance. Founded in 2019 and headquartered in West Hollywood, California, the privately held company offers FDA-cleared tools (e.g., Second Opinion) and practice analytics via subscription. Pearl raised a Series B in 2024 to expand its medical device and practice management software portfolio.