Vetted Prompt Engineering Professionals

Naga Renuka Kandi

Screened

Junior Software Engineer specializing in cloud, full-stack development, and Generative AI

Remote, USA2y exp

“Built and shipped a production Chrome extension (Promptly) that lets users select text on any webpage and transform it in place (rewrite/shorten/translate) using on-device AI plus external LLMs. Implemented a custom lightweight orchestration layer for prompt chaining, context flow, and output validation, and tackled tricky browser Selection API issues to preserve formatting while keeping the UX simple and fast.”

Fangjian Xiong

Screened

Junior Machine Learning Engineer specializing in NLP and biomedical entity extraction

Boston, MA2y exp

“Built and deployed a production LLM-powered biomedical knowledge extraction pipeline that processed millions of papers to identify tools/techniques and produce a unified knowledge graph via active learning NER (Prodigy + spaCy transformers) and entity linking (Bio-tools/Wikidata). Addressed hard NLP engineering challenges like WordPiece span-offset alignment and scaled inference over ~1.5M documents using batching/caching, containerized services, async workers, and orchestration with Prefect/Airflow.”

Sai Nekkanti

Screened

Mid-level Data Scientist / ML Engineer specializing in secure GenAI and financial compliance

Mount Laurel, NJ4y exp

“Built a production "sentinel insight engine" to tame information overload from millions of product reviews and support transcripts, combining Azure OpenAI (GPT-3.5) zero-shot classification with a fine-tuned T5 summarizer to generate weekly actionable product insights. Demonstrated strong MLOps/production engineering by adding drift monitoring with embedding-based detection, integrating REST with legacy SOAP/queue-based CRM via FastAPI middleware, and scaling reliably on Kubernetes with HPA.”

Nishad Kane

Screened

Mid-level Data Scientist & AI Engineer specializing in RAG, agentic AI, and production ML

5y exp

“AI/data engineer who built a production LLM-powered schema drift detection system (LangChain/LangGraph) to catch semantic data changes before they break downstream analytics/ML. Deployed on AWS with Docker/S3 and implemented an LLM-as-a-judge evaluation framework to improve trust, reduce hallucinations, and control false positives/alert fatigue. Collaborated with non-technical risk/business analytics stakeholders at EY by delivering human-readable drift explanations that improved confidence in financial analytics dashboards.”

Yaw Prempeh

Screened

Director-level Performance Marketing Leader specializing in paid social and Google demand platforms

New York, NY5y exp

“Performance marketer managing high-spend ecommerce and lead-gen accounts across Meta and Google, including $120K in 3 weeks for a women’s fashion peak sale where they drove 13x blended ROAS vs a 6x target (Oct 2025). Experienced in full-funnel conversion setups (PMax, RSA, Advantage+, DPA), audience segmentation for incremental acquisition, and creative testing/refresh to combat ad fatigue; also ran creative messaging tests at Scholastic focused on teacher lead conversion.”

Sreelekha Vuppala

Screened

Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms

USA4y exp

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

Harini Vinu

Screened

Intern Software Engineer specializing in cloud, big data, and test automation

New York, United States1y exp

“Internship experience at Qualitest building and deploying an LLM-powered test automation system that reduced manual test creation and improved efficiency (~40%). Demonstrates strong production engineering for LLM systems (timeouts/retries/monitoring/caching, prompt optimization, batching) and has scaled workflows to 100+ concurrent jobs; also has orchestration experience with AWS Step Functions and Kubernetes.”

Prashant Salunke

Screened

Mid-Level Software Development Engineer specializing in full-stack and cloud-native systems

Chicago, IL4y exp

“Backend engineer who has shipped production LLM-powered features, including an AI-assisted developer tool on AWS (Spring Boot) and a blog platform capability using embeddings + Elasticsearch for semantic retrieval and LLM-generated summaries/recommendations. Demonstrates practical tradeoff management (quality/latency/cost), guardrails to reduce hallucinations, and evaluation-driven iteration using real user queries and observability via ELK.”

“Built and owns the full ProteinMenus AI pipeline end-to-end, spanning the iOS client, FastAPI backend, Gemini integration, Firestore, and Cloud Run deployment. Strongest signal is full-stack product ownership in an AI-driven consumer workflow, including monetization logic via an atomic credit system and architecture choices optimized for fast iteration after launch.”

“Built production LLM/RAG systems during a UPS internship, including a shipment knowledge agent used across 15+ hubs worldwide and a multi-agent PDF RAG workflow. Stands out for combining hands-on enterprise integration with rigorous evaluation, hallucination reduction, and efficient fine-tuning techniques like LoRA.”

“Built both traditional internal tooling and LLM-powered systems during an internship, including a React/Python/AWS calculator onboarding platform and a production-style ROS2 RAG assistant over 10K+ documents. Stands out for combining full-stack delivery, stakeholder coordination, and practical AI reliability work like retrieval tuning, source-grounded answers, and low-confidence fallbacks.”

“AI Engineer with hands-on ownership of a production multi-agent RAG platform in financial services, spanning experimentation, architecture, deployment, monitoring, and iterative optimization. Stands out for measurable impact: 35% retrieval relevance improvement and nearly 50% reduction in manual operational analysis effort, plus strong experience making enterprise LLM systems safer and more reliable in production.”

“Full-stack engineer with 6+ years of experience building high-scale internal products and AI-powered workflows, including a U.S. Bank payment operations dashboard handling 500k+ transactions and real-time analyst collaboration. Stands out for true end-to-end ownership—from React/TypeScript frontend architecture to Node/Spring services, PostgreSQL/Redis optimization, Kubernetes deployment, and Datadog monitoring—plus measurable impact on adoption, latency, and analyst efficiency.”

Supreet Purthpli

Screened

Mid-level AI/ML Software Engineer specializing in cloud-native MLOps and FinTech

San Francisco, CA4y exp

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

Ajay Desai

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and backend platforms

USA4y exp

“Built an AI-native legal research platform that automated analysis across 100,000+ dense legal documents, combining LLM workflows, async backend architecture, and conversational retrieval in production. Also brings cross-domain experience in investment-analysis agents and healthcare claims/billing systems, with a strong emphasis on reliability, deterministic orchestration, and safe handling of messy operational data.”

“AI/full-stack engineer with experience spanning startup product building at Twinly, enterprise analytics at Zoho, and high-stakes life sciences ML at Wave Life Sciences. Stands out for combining React/TypeScript + FastAPI product execution with rigorous AI evaluation, retrieval optimization, and human-in-the-loop design, delivering measurable outcomes like 75% fewer analytics requests, 20% fewer failed experiments, and MVP delivery 3 weeks early.”

“Built and deployed LLM-powered agentic systems including a multi-agent travel planning assistant using LangChain, RAG (FAISS), real-time APIs, and a supervisor agent to manage coordination and reduce hallucinations. Also developed a Text-to-SQL system with schema-aware validation guardrails, and collaborated with drilling domain experts at CNPC USA to build an ML model predicting rate of penetration (ROP).”

Nithyashree Raghunathan

Screened

Mid-level Full-Stack AI Engineer specializing in agentic systems

Santa Clara, CA5y exp

“QA/data pipeline engineer with hands-on AI product building experience, spanning enterprise AWS migration testing for Belgium postal services and personal multi-agent systems in fintech and recruiting. Stands out for combining rigorous validation and production stability work with modern LLM orchestration, guardrails, and messy-document normalization workflows.”

“Full-stack product engineer who has built both operational systems and enterprise AI copilots in production. They owned an AI-powered inventory platform end-to-end, driving a 45% drop in stock issues, and also shipped a Microsoft Teams-based HR/IT copilot using RAG and workflow automation that reduced repetitive support queries by roughly 30%.”

Murali Marupudi

Screened

Mid-level Backend Engineer specializing in Python microservices and scalable cloud systems

Jersey City, NJ4y exp

“Backend engineer focused on high-throughput Python/Flask systems on AWS, with strong scaling and performance tuning experience (e.g., PostgreSQL join reduced from ~3s to <200ms; background aggregation cut from 10 minutes to <90 seconds with 8x throughput). Has also integrated ML model serving into production APIs (churn prediction) using Celery/Redis batching and AWS Lambda/S3, and designed secure multi-tenant architectures with PostgreSQL schema isolation and row-level security.”

“Product leader with experience rebuilding teams and platforms in both healthcare and AI-enabled SaaS environments. Most notably rebuilt Uniform Teeth’s product organization, EMR, and patient app after a restructuring, contributing to major clinical efficiency gains and the company’s acquisition by Impress in 2022. Brings a strong human-in-the-loop AI philosophy, plus experience leading PMs, design, and engineering through high-change environments.”

“Built and owned Citigroup's ASTRA AI-powered test case generation platform end to end, from full-stack product experience to multi-agent LLM orchestration and RAG infrastructure. Drove test coverage from 40% to 95%, cut generation time from hours to minutes, and scaled the feature to 300+ daily users across 32 enterprise projects with sponsorship from Citi's CIO and Head of Engineering Excellence.”

“Built an internal AI-powered document search and Q&A platform at BNY that let employees query company documents in natural language and get grounded answers in seconds. Brings practical full-stack and LLM systems experience across React/TypeScript, FastAPI, Pinecone, OpenAI, and Claude, with clear emphasis on retrieval quality, hallucination reduction, and production monitoring.”