Vetted Code Reviews Professionals

JYOTHI N

Screened ReferencesStrong rec.

Senior Data Scientist specializing in analytics, experimentation, and BI on AWS

Austin, TX7y exp

“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”

Vijay Mohan

Screened ReferencesStrong rec.

Executive technology leader specializing in AI, cloud platforms, and enterprise software

Nashua, NH25y exp

“Founder of Braindump (redfly.ai), an AI companion for memory and personal organization, who has already raised $60,000 from Alchemist Accelerator. Brings hands-on experience with customer discovery, technical validation, and VC pitching, and stands out for a first-principles approach to building products with dramatic performance and cost improvements.”

“Backend engineer with hands-on ownership of Python/Flask microservices and recommendation systems across edtech and telecom. Deployed and operated real-time personalization/recommendation platforms on AWS EKS with Jenkins-based CI/CD, GitOps-style declarative configs, and strong observability practices. Has migration experience moving legacy mixed environments to modern containerized Kubernetes and built Kafka pipelines feeding ML services while managing schema evolution.”

“Worked on onboarding a Middle East logistics client processing thousands of invoices/month, building a production-ready pipeline that routes known vendor PDFs to deterministic regex parsers via Tax ID matching and falls back to LlamaParse for unknown layouts. Added financial consistency validation plus human-in-the-loop review and logging/metrics to continuously reduce LLM usage and improve template coverage.”

“Full-stack engineer who has owned customer-facing AI recommendation and analytics dashboards end-to-end (backend APIs/data processing through React UI, deployment, and monitoring). Demonstrates strong systems thinking around scaling microservices—using observability, caching, async workflows, and resilience patterns—and also built an internal ops dashboard that became the default tool for on-call incident reviews.”

Praneeth Regonda

Screened

Mid-level Full-Stack Engineer specializing in FinTech and AI platforms

New York, NY4y exp

“Full-stack engineer with 3 years of AI/ML experience who has shipped production LLM workflows, including a Bloomberg triage dashboard that cut manual processing by 35%. Combines React/TypeScript product sense with AWS/Spring/Lambda backend architecture and unusually strong practical judgment around evals, trust, retrieval, latency, and UX for real-world AI systems.”

“Built a production-grade agentic document intake system that converts PDFs into structured records with strict schema validation, confidence-based retries, and a human review UI. Demonstrates strong practical judgment around making LLM systems reliable in enterprise workflows, including custom orchestration, observability, and continuous evals rather than relying on off-the-shelf abstractions.”

“Full-stack/product-minded engineer with recent experience in both an early-stage AI startup and a B2B payments marketplace. Stands out for building a pgvector-based semantic cache that reduced LLM latency by 35% and for shipping audit-heavy payment infrastructure with Stripe/Plaid, idempotent webhook handling, and major reconciliation query optimizations.”

“Graduate robotics researcher (Georgia Tech/Georgia Tech Research Institute) who helped modernize the Georgia Tech Robotarium by migrating its comms stack from MQTT to ROS2 across MATLAB/Python and updating embedded Teensy firmware for new sensors. Currently validating ToF distance sensors and integrating IMUs, with planned GTSAM factor-graph SLAM sensor fusion; also debugged and improved a decentralized coverage-control algorithm at swarm scale (1000–2000 agents) using computational geometry and literature-backed methods.”

“Engineering leader with BCG consulting background who has built roadmaps and scaled AI and data platforms for pharma and manufacturing clients. Led architecture shifts (Django monolith to event-driven microservices) for high-volume IoT SaaS products, improving deployment speed and enabling zero-downtime releases. Also established a near-shore engineering team in São Paulo and has managed distributed teams across multiple countries, leveraging strong stakeholder communication and a prior professional acting background for storytelling.”