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Vetted Prompt Engineering Professionals
Pre-screened and vetted.
Bhanu Gummadi
Mid-level Backend Software Engineer specializing in cloud-native microservices and FinTech
“Backend-focused engineer with Mastercard experience building and operating high-volume transaction-processing microservices. Has owned customer-facing banking services end-to-end and built an internal on-call analytics tool that centralized logs/metrics with real-time filtering to speed root-cause analysis and reduce incident investigation time.”
“Designed and deployed a production LLM agent platform at the National Institutes of Health to reduce time spent searching fragmented internal documentation, combining RAG grounding with multi-step tool-calling workflows and integration into legacy services via inference APIs. Emphasizes production-grade reliability through automated evaluation on real queries, guardrails/safe-failure behaviors, and ongoing A/B testing and monitoring, and has experience translating non-technical stakeholder goals into measurable success metrics.”
Harikiran Jangam
Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG systems
“Backend engineer who built and evolved a PHI-compliant RAG system (FastAPI + LangChain + embeddings/FAISS) for internal document search and summarization, delivering <400ms p95 latency at ~2,500 daily requests and measurable impact (30% faster investigations, +17% retrieval relevance). Demonstrates strong security and rollout discipline (RBAC/RLS/JWT, redaction/audits, shadow mode, dual writes, canaries) and a focus on reducing hallucination risk via grounded guardrails and confidence-based fallbacks.”
Sai Rahul Dasari
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and healthcare RAG systems
“Built and deployed a production clinical claim validation RAG system at GE HealthCare that automated nurses’ patient-history/claims checks, cutting manual review time by ~65%. Designed the full stack (retrieval, embeddings, Pinecone, prompt/verification guardrails, FastAPI backend) with PHI-compliant anonymization via NER and orchestrated pipelines using Airflow, Azure ML Pipelines, and MLflow with drift monitoring.”
Gary Chang
Senior Full-Stack Software Engineer specializing in AI-first cloud-native systems
“End-to-end engineer who has productionized AI automation and RAG capabilities, building full-stack systems (React/Node/Redis/Postgres + vector DB) with evaluation-driven quality gates and monitoring. Reported ~60% reduction in manual ops time and major turnaround improvements, and has experience modernizing legacy systems safely via feature flags and parallel runs while working across product, data, and ops teams (System1).”
Pranita Agrawal
Mid-level Software Engineer specializing in Java microservices and AWS
“TypeScript backend/full-stack engineer who owned an internal business workflow platform end-to-end in production, including API/data design, relational DB integration, and enterprise integrations. Has hands-on experience operating workflow processing services with Kafka-style event-driven patterns, idempotency, exponential backoff retries, dead-letter queues, and strong observability, plus API design with OpenAPI/Swagger and token-based auth.”
Mahesh Kota
Mid-level Full-Stack Java Developer specializing in cloud-native FinTech and Healthcare platforms
“Backend engineer with production experience building and scaling a Java/Spring Boot payment processing API on AWS (PostgreSQL/Redis) handling a few thousand RPS, including deep performance debugging (connection exhaustion) and observability (CloudWatch, Actuator, Zipkin). Also shipped application-layer AI features (OpenAI email summarizer with feedback loop, ~40% faster agent response times) and designed reliable multi-step workflow orchestration with retries and manual escalation, plus strong SQL tuning and Python engineering practices.”
Tejasri Joshi
Mid-level Machine Learning Engineer specializing in Healthcare AI and Generative AI
“Analytics professional with Intuit experience spanning modern data stack work, behavioral segmentation, and applied AI. They built dbt/Snowflake pipelines powering retention and churn dashboards, automated feedback classification with OpenAI/LangChain, and partnered closely with product and marketing teams to turn analytics into onboarding, targeting, and lifecycle messaging decisions.”
Shashwat Negi
Mid-level Software Engineer specializing in AI/ML and full-stack systems
“Data Scientist (2–3 years) at ZS Associates who has built and productionized agentic LLM systems, including a LangGraph-based multi-LLM prompt-optimization pipeline for entity extraction deployed as a Spring Boot microservice via Jenkins. Also built an Insightmate.ai chatbot and improved its RAG accuracy by diagnosing vector retrieval issues and implementing HyDE query expansion, while partnering with sales and pharma stakeholders to drive adoption (e.g., Zimmer Biomet platform migration into a multi-year partnership).”
Christian Babcock
Mid-level Software Engineer specializing in Unity simulations and gameplay systems
“Unity/C# gameplay engineer with strong multiplayer systems experience, including shipping a DarkRift-based server-authoritative architecture with prediction/reconciliation and measurable gains: frame time improved from ~30ms to 18ms and packet sizes dropped by roughly 40%. Also brings practical thinking around AI-assisted content tooling, using async service layers, JSON pipelines, and validation rules to support designers without disrupting production workflows.”
Mehar Matta
Junior Marketing Analytics professional specializing in B2B SaaS and AI-driven insights
“Analytics professional with hands-on experience building SQL and Python workflows for marketing, funnel, and revenue reporting across tools like Salesforce, Google Analytics, and Power BI. They stand out for turning messy, multi-source data into trusted reporting layers, aligning sales and marketing on shared KPI definitions, and using segmentation/cohort analysis to improve campaign targeting and conversion performance.”
Will Long
Principal Gameplay Programmer specializing in game systems and cross-platform architecture
“Principal-level game engineer with deep Unity/C# systems experience who rebuilt a core procedural reels framework for long-term extensibility and partner-team adoption. Also stands out for early, hands-on AI/LLM gameplay work in a multiplayer text game, including NPC memory/personality systems, custom model deployment, and cost-scaled inference infrastructure, plus shipped VR RTS experience across Quest/Rift-era and mobile VR platforms.”
Sagar Sidhwa
Senior AI/ML Engineer specializing in LLMs, MLOps, and predictive analytics
“ML/AI engineer with hands-on experience building production MLOps systems for predictive maintenance and demand forecasting, including deployment, monitoring, and iterative retraining. Also shipped a RAG-based employee onboarding chatbot integrated with ServiceNow APIs and reports business impact of roughly $300k/month in reduced stockout and overstock costs.”
Payal Jadhav
Mid-level Software Engineer specializing in full-stack cloud and backend systems
“Full-stack JavaScript engineer (React/Node/Vue) who has operated like a maintainer by owning an internal component library with Storybook-style examples, documentation, and non-breaking versioning. Demonstrated strong performance engineering on a source code review service—profiling bottlenecks, fixing N+1 queries, adding caching, and trimming payloads to cut latency (e.g., ~100ms to <50ms) while rolling out incremental, test-backed improvements.”
“Founding/principal product marketer who specializes in building zero-to-one B2B growth systems in ambiguous environments. At Real Work Labs, they diagnosed why an AI voice product had no traction, repositioned it for trust-sensitive home service contractors, and drove measurable pipeline results while also building the underlying attribution and handoff infrastructure across HubSpot, Salesforce, Slack, and Gong.”
Chandana G
Mid-level Full-Stack Engineer specializing in cloud microservices and AI-powered platforms
“Full-stack engineer with hands-on experience building real-time operational products across banking, insurance, and startup e-commerce environments. They’ve owned features end-to-end—from React/TypeScript dashboards and Redux performance tuning to Spring Boot, Kafka, AWS Lambda, and production monitoring—and have also shipped 0→1 capabilities where business impact was immediate, such as reducing overselling through inventory visibility.”
daniel fisher
Junior Full-Stack Developer specializing in FinTech and data systems
“Built a prediction market analytics website from scratch to analyze volume and correlations in decentralized markets, drawing on a sports and finance background. Shows strong early-stage product instincts, backend ownership, and a pragmatic approach to scalability, security, and AI-assisted data workflows.”
Jeremy Greenawalt
Principal AI Engineering Leader specializing in LLM integration and developer workflows
“Built and launched a production customer-service LLM agent/MCP system for a large website, enabling reps to search across millions of SKUs and manuals that were previously impractical to navigate manually. They emphasize maintainable AI architecture, staged rollouts, guard-agent evaluation, and multi-agent code review workflows spanning accessibility, performance, and security.”
Angela Churchwell
Principal Software Engineer specializing in enterprise AI platforms
“Built a production-grade LLM document processing and workflow orchestration platform at CBRE for internal operations teams, handling highly variable long-form documents with a reusable architecture involving 50+ coordinated LLM calls per request. Stands out for treating agentic systems like distributed backend infrastructure, with strong emphasis on evaluation, observability, reliability, and vendor-agnostic orchestration across Bedrock, Vertex AI, and OpenAI.”
Surya Ravula
Junior Software Engineer specializing in backend systems and AI automation
“Backend/platform engineer with Boston Scientific experience building secure healthcare integrations, resilient AWS data pipelines, and a production internal LLM support chatbot. Stands out for combining legacy-system modernization, strong reliability practices, and measurable operational impact in regulated healthcare environments.”
Srivatsava Vuppala
Junior Software Engineer specializing in AWS backend and Generative AI
“Engineer focused on AI-assisted software development and enterprise legacy modernization, with hands-on experience designing multi-agent workflows for code analysis, business logic extraction, BRD generation, and validation. Stands out for combining prompt design and agent orchestration with strong engineering discipline, including testing, CI/CD, and human review checkpoints.”
Lokesh Jain
Senior AI/ML Engineer specializing in supply chain and healthcare systems
“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”
Trishal Mudunuri
Mid-level Full-Stack Engineer specializing in healthcare platforms and cloud-native systems
“Built both a React/Supabase kanban product and CodeVoyage, a multi-agent platform for navigating large TypeScript/Node.js codebases. Stands out for being unusually rigorous about AI-assisted development: they quantify AI usage, manually verify generated code, and have firsthand experience debugging failures in persistence layers, retrieval quality, and long-context agent orchestration.”
Rachana Munugala
Mid-level Backend Engineer specializing in distributed systems and FinTech AI platforms
“Engineer at Morgan Stanley working on AI-enabled trade surveillance and compliance routing systems. They’ve built and monitored chained agent workflows for retrieval, risk classification, and alert routing, with strong emphasis on auditability, hallucination prevention, and regulated-environment reliability.”