Adithya Rajendra

About

Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.

Experience

Education

Key Strengths

• Built end-to-end medical imaging ML pipeline (retinal vessel segmentation → biomarker extraction → disease classification)
• Data quality and deduplication at scale (10M+ daily SAP records) with ~99% accuracy via automated validation
• Model selection/validation focused on recall for medical use cases; comparative evaluation across multiple classifiers
• Delivered major workflow automation impact: data onboarding setup reduced from 8 hours to 12 minutes; enabled 15+ data engineers
• Improved knowledge retrieval with RAG + embeddings; reduced manual querying workload by 82% (hours saved weekly)
• Productionization discipline with stakeholder validation and human-in-the-loop checkpoints for AI-generated financial reports

Reference Highlights

• Quick learner (picked up Python and PySpark fast)
• Delivers tools that remain in use and work reliably
• Understands requirements/vision and iterates to a strong end result
• Strong problem-solving (overcame obstacles through research and own solutions)
• Structured communicator
• Able to explain technical concepts to non-technical stakeholders
• Broad understanding of his work
• Strong ownership and accountability for outcomes
• Proactive in asking clarifying questions
• Works independently to deliver
• Highly dedicated (supported production issues even on holidays)
• Strong team player
• Puts team success over personal interests
• Continuous improvement mindset
• Very strong Python proficiency
• Deep data engineering expertise (go-to advisor)
• Strong ML/NLP solution guidance and practical implementation experience
• Able to solve complex implementation challenges (AI assistants with data protection constraints)
• Proactive ownership and initiative (pushes modernization vs. staying on legacy tech)
• Leadership and ability to lead teams to delivery
• Delivers on promises within set timeframes
• High quality output (better than market standards per reference)
• Collaborative and inclusive team style (ensures all voices are heard)
• Strong communication across different groups
• Extensive professional network and ability to connect people/resources
• Innovative, cutting-edge thinking; anticipates issues early
• Thoughtful decision-making