.png){kind=logo}
Ponaganti Sanjana
Mid-level AI/ML Engineer specializing in NLP, MLOps, and FinTech
Connect with Ponaganti
Ponaganti already has a relationship with Reval, so a warm intro from us gets a much better response than cold outreach.
Recommended
Already have an account?
About
ML/AI engineer with production experience at S&P Global and Accenture, focused on regulated, enterprise-grade systems. Built end-to-end financial risk and credit default models with >90% precision and 12% fewer false positives, and is currently developing secure RAG pipelines on AWS SageMaker for enterprise insight extraction.
Find your next great hire
Our AI agents source, screen, and vet candidates for your open roles. Get qualified candidates within 48 hours.
Key Strengths
- owned end-to-end ML system delivery from ETL to deployment and monitoring
- production ML in financial risk and credit default forecasting
- combined structured financial data with unstructured ESG data
- strong model interpretability for regulated environments using SHAP and LIME
- deployed scalable ML APIs on Azure ML and Kubernetes
- built drift monitoring and automated retraining workflows
- achieved over 90% precision in pilot risk forecasting
- reduced false positives by 12% in production
- building enterprise RAG pipelines on AWS SageMaker
- implemented security controls including RBAC and encryption
Like what you see? We'll introduce you to Ponaganti directly.
Experience
Education
Languages
Browse Similar
Similar Candidates
Senior Machine Learning Engineer specializing in multimodal AI and biomedical data
Senior Machine Learning Engineer specializing in LLMs and Generative AI
Senior Software Engineer specializing in AI/ML evaluation and full-stack systems
Mid-level AI/LLM Engineer specializing in generative AI and ML systems
Mid-level AI/ML Engineer specializing in fraud detection and customer lifetime value modeling
Akhilesh Patil
Junior AI/ML Engineer specializing in FinTech and generative AI
“Built an end-to-end AI bug triage dashboard that combined React/TypeScript, FastAPI, Postgres, and classical ML to reduce manual engineering triage work by about 40%. Stands out for pragmatic, product-minded AI engineering: choosing interpretable models when they were sufficient, designing human-in-the-loop UX for trust, and separately building an agentic RAG project with vector search, Neo4j knowledge graphs, and reranking.”
Interested in Ponaganti?
We'll personally introduce you - no strings attached.
Build your dream team with Reval
Our AI agents source, screen, and vet candidates for your open roles. Get qualified, high-intent candidates on your desk within 48 hours.
Discover more candidates like Ponaganti
Search across thousands of pre-screened, high-quality, high-intent candidates on Reval.
Search Talent