Senior AI Data Scientist (Internal AI & Infrastructure)

Role Profile: Senior AI Data Scientist (Internal AI & Infrastructure)

Location: San Jose CA - (Hybrid 3 days onsite)

Professional Summary

Senior Data Scientist with over 12 years of experience specializing in production-grade GenAI and Machine Learning systems within complex enterprise environments. Expert at architecting LLM-powered assistants and predictive models that streamline internal operations, reduce technical debt, and drive measurable ROI. Proven capability in deploying high-performance AI solutions on Kubernetes and distributed cloud architectures to automate internal workflows and diagnostic processes.

Core Responsibilities for Internal AI

• Internal Product Innovation: Transform large-scale internal data (logs, tickets, telemetry) into production-ready GenAI solutions to support the mission and enhance employee productivity.
• Infrastructure-Aware AI: Design and champion AI models that balance immediate internal feature requests against the long-term technical health of production environments.
• Strategic AI Consulting: Partner with stakeholders to identify high-impact AI opportunities, demonstrating how data science can optimize internal department OKRs.
• Advanced Analytics: Deliver production-ready models for internal resource forecasting, system health monitoring, and automated fault diagnosis.
• Rigorous Evaluation: Implement evaluation frameworks using RAGAS and DeepEval to ensure internal AI tools meet strict accuracy and latency standards.

Technical Skills for Integration

• GenAI Stack: RAG, Lang Graph, Langfuse, Semantic Search, Re-ranking, and Vector Databases (OpenSearch).
• ML & Statistics: Expert mastery of Regression, Clustering, and Neural Networks, with the ability to design controlled experiments and A/B tests.
• NLP Expertise: BERT, Transformers, and RASA (DIET Classifier) for building sophisticated internal conversational interfaces.
• DevOps & Infrastructure: Professional experience with Kubernetes, Helm, Microservices, and REST APIs to ensure AI models are seamlessly integrated into the ecosystem.

Qualifications

• Education: Bachelors or Masters in Data Science , Computer science
• Seniority: 10+ years of end-to-end ML delivery in production
• Interview Ready: Prepared for deep-dive technical sessions, including live whiteboarding of AI architectures and statistical modeling.