Jayashree Raman

About

Senior product engineering and data architecture leader who has owned end-to-end solutions for enterprise analytics and legal search platforms, including work for Sony Pictures Entertainment and Accenture Legal. Combines executive-level architecture and team leadership with hands-on experimentation in modern AI systems, recently building a multimodal Hugging Face agent that significantly exceeded its benchmark score.

Key Strengths

• End-to-end product engineering ownership from business requirements to architecture and roadmap
• Strong data architecture leadership across conceptual, logical, and physical models
• Pragmatic build-vs-buy decision making with technical debt risk management
• Cross-functional collaboration with business stakeholders, data stewards, and technical teams
• Complex multi-source data integration and source-of-truth resolution
• Data quality remediation through upstream fixes and staging-layer cleansing
• Leadership of small to mid-sized engineering teams with managers, architects, data engineers, and data scientists
• Process-oriented team scaling using Agile, RACI, and lead-based performance management
• Hands-on quality improvement through bug pattern analysis, checklists, and unit test enhancements
• Recent hands-on AI prototyping with a Hugging Face agent built in Python using Gemini and MongoDB
• Practical LLM experimentation and hallucination mitigation across multiple models
• Experience designing analytics/search platforms in media and legal domains
• Built integrated analytics hub used as a foundation for data science models
• Developed an AI agent that scored 55 against a passing score of 30
• Defined and executed technology roadmaps aligned to business strategy (legal contract search solution for global legal team)
• Led target-state architecture and stakeholder alignment, then delivered via Agile with iterative feedback loops
• Resolved complex data-source challenges (poor documentation, complex proprietary contract metadata DB) through source mapping and transformation logic
• Improved data reliability by driving fixes at the source plus implementing data quality checks with monitoring/alerts
• Scaled an AI/ML engineering effort from 0 to a core onshore team of 6 plus offshore testing/validation with clear team structure and leads
• Cross-functional, global team leadership across time zones with disciplined operating cadence (bi-weekly sprints, daily scrums)
• Pragmatic architecture decisions to match team skill sets (Accenture handled complex SQL/pipelines; client handled OBIEE front-end)
• Designed scalable data integration by chunking and parallelizing Redshift-to-Salesforce pipelines to handle large volumes
• Built distributed-team accountability and innovation via async-first practices, peer reviews, and knowledge-sharing sessions (brown bags/lunch & learns)

Experience

Education

Awards

• National Merit Scholarship

Skills

Languages

Certifications