Vetted Embeddings Professionals

Jayadeep Nukala

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in enterprise AI and FinTech integrations

San Francisco Bay Area, CA2y exp

“Built and deployed an enterprise AI testing solution at a startup, then customized and scaled it inside Citigroup over the course of a year to support 40+ projects and 1,000+ daily users. Brings hands-on production experience with multi-agent LLM workflows, RAG, enterprise deployment infrastructure, and real-world incident handling in AI-driven data pipelines.”

“ML/AI engineer with hands-on experience shipping production computer vision and GenAI systems, including a fabric defect detection platform that combined vision models with agentic LLM workflows to reach 89% human-inspector agreement at 200 ms latency. Also built a RAG-based code QA tool for developers and emphasizes production monitoring, evaluation, caching, and reusable Python service design.”

“Full-stack engineer with experience spanning a small healthcare startup and Groupon-scale personalization systems. Stands out for building HIPAA-compliant healthcare workflows end-to-end while also shipping recommendation and LLM-enabled platforms used by millions, with strong depth in React/Next.js, Node.js, Python, AWS, and scalable event-driven architecture.”

“Internship experience building an end-to-end medical AI pipeline that extracts and normalizes messy medical PDFs, fine-tunes BioBERT to classify tumor-related statements (including negation/ambiguity handling), and integrates image-model outputs (MedSAM/GroundingDINO) for tumor localization and classification. Also worked on an LLM/RAG system to draft IPO prospectuses using retrieved regulatory/financial sources (including SEC EDGAR) with structured prompts to reduce hallucinations.”

Manasa Pantra

Screened ReferencesStrong rec.

Junior Software Engineer specializing in AI, LLM systems, and full-stack development

Stony Brook, NY2y exp

“Product-focused full-stack engineer at startup (Zippy) who shipped a production multi-agent AI system for restaurant operations plus payments workflows. Built end-to-end: RAG grounded on a Notion knowledge base, structured function-calling task routing, FastAPI/JWT multi-tenant backend, and a polished React+TypeScript owner dashboard. Has real production incident experience (duplicate Stripe webhooks) and reports ~94% task-routing accuracy under load.”

“Applied NLP/ML in healthcare and financial services, including fine-tuning BERT on unstructured EHR text and building embedding-based similarity search for clinical concepts. Also redesigned a Wells Fargo fraud detection data pipeline using modular Python + AWS Glue/Step Functions, cutting runtime ~40% with improved monitoring and reliability.”