Vetted AI Agents Professionals

“Built and deployed a production LLM-assisted recommendation and insights platform that unifies structured, semi-structured, and unstructured data via a modular ingestion pipeline, canonical schemas, embeddings, and late-fusion modeling. Experienced in operationalizing ML/LLM systems with Airflow and Kubernetes (Dockerized services, autoscaling, rolling updates) and emphasizes reliability through layered testing, guardrails, monitoring, and A/B experimentation while partnering closely with non-technical stakeholders.”

“LLM/agentic AI builder who deployed a production ITSM automation agent on Google ADK integrating ServiceNow and FreshService, with strong safety guardrails (human-approval gating and runbook-only command execution) and rigorous evaluation (500 synthetic tickets; 80%+ false-positive reduction). Also partnered with finance to deliver an AI agent that automated invoice/SOW retrieval and monthly reporting to account managers, reducing manual back-and-forth.”

“Contingency recruiter specializing in AI and data roles, currently managing 15 live requisitions across 6 clients (consultancies and financial institutions). Uses a structured, color-coded Google Sheets system to track pipeline stages and notes, and reports a typical 2-week turnaround from intake to placement.”

Monish Reddy Bonthu

Screened

Mid-Level Full-Stack Engineer specializing in real-time systems and FinTech

FL, USA5y exp

“Backend engineer with hands-on experience modernizing a real-time logistics/tracking platform from a tightly coupled polling architecture to a service-oriented/microservices design using Node.js and WebSockets. Emphasizes contract-first FastAPI development, defense-in-depth security (JWT/OAuth, RLS/Supabase), and safe incremental migrations with feature flags and strong observability, delivering sub-second updates and improved performance under peak load.”

“Production-focused ML/AI engineer with hands-on ownership across classical ML and GenAI systems, from CV/NLP services to enterprise RAG. Stands out for combining research-to-production execution with measurable business impact: 40% processing-efficiency gains, 35% fewer support tickets, 5x latency improvement, and 3x throughput gains while maintaining safety and quality.”

“Project/program manager with senior-level ownership across SaaS implementations, cloud migrations, AI agents, and DevOps initiatives. Notably led an Azure AI Studio-based AI analysis tool from start to finish and managed an enterprise healthcare technology portfolio with interdependent AI, data extraction, Copilot, and web application projects.”

“Built a full-stack dependency impact analysis product ('Blast Radius') that mapped runtime service relationships and reportedly reduced deployment incidents by 40%. Also developed AI evaluation and security benchmarking systems, including WebSEC Arena and a lyric-generation tool fine-tuned on 300,000 song lyrics, with academic interest strong enough to spur a research paper effort.”

“Founding engineer at an early-stage agent marketplace who built the core job lifecycle, live run viewer, and LLM-based verification/settlement flow that gated escrow release. Also worked on a multi-agent diagnostic system using a blackboard architecture with specialized models, structured outputs, and human-in-the-loop review—showing unusual depth in both product engineering and applied AI systems.”

“Full-stack engineer with experience spanning enterprise document platforms and AI-powered clinical education products. Built secure TypeScript/Next.js features end to end, designed multi-model LLM workflows with validation and monitoring in production, and modernized a legacy PHP monolith through blue-green deployment and incremental migration without outages.”

“Built a real-time AI meeting assistant using a Chrome extension that streams audio to a backend LLM workflow with transcription and RAG, then hardened it for production with queue-based streaming, async pipelines, security controls, and full observability. Also has hands-on startup sales experience, partnering with customers to define measurable technical win conditions (latency/accuracy) to close deals and drive adoption.”

“Unity gameplay programmer with experience on TT Games' LEGO Star Wars Battles, including adding full controller support to a touch-first unit deployment system and implementing new networked unit mechanics in a Photon-based multiplayer layer. Has worked in client-server architectures (including Metaplay-backed servers), with a focus on refactoring for reusable combat/damage code and scalable analytics instrumentation.”

“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”

“Built and owned a production-grade RAG/LLM support automation system on AWS using GPT-4, Pinecone, FastAPI, and Redis, taking it from initial experimentation through deployment, monitoring, and iterative improvement. Their work reduced support workload and ticket volume by about 40%, improved CSAT and self-service resolution, and they also created shared Python/LLM infrastructure that accelerated other teams' delivery from weeks to days.”

“Software engineer with a pragmatic, production-oriented approach to AI-driven development, using AI to accelerate coding while keeping human oversight on correctness, architecture, and final decisions. Has hands-on experience with agent-style AI workflows and has led the design and coordination of AI-agent systems with a strong emphasis on reliability, performance, and end-to-end execution.”

“Early major technical hire who helped build fintech startup Ugami from MVP to near Series A, while supporting bridge round and Series A fundraising with technical materials for leadership. Also grew from intern to lead engineer at a venture-backed product shop, giving him firsthand exposure to investor expectations, startup incubation, and practical AI opportunities with tight scope and strong unit economics.”

Meghkumar Patel

Screened

Intern-level Software Engineer specializing in backend, cloud, and AI systems

Vadodara, India1y exp

“MS Software Engineering candidate at SJSU with hands-on full-stack and applied AI experience, including building DataTrust, a secure enterprise RAG assistant with ABAC-style access control, audit logging, and grounded-answer evaluation. Also built CareerPilot, an AI interview-prep product that turns LLM and speech-to-text outputs into structured, user-friendly feedback for students and job seekers.”

“Full-stack engineer who has shipped and operated generative-AI chat/QA features end-to-end, including a RAG-based pipeline with guardrails and cost/latency monitoring in production. Experienced with React/TypeScript + Node/Postgres architectures, Dockerized deployments to AWS (EC2) via GitHub Actions CI/CD, and building reliable ingestion/ETL systems with idempotency, backfills, and reconciliation.”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Founder building SmartTreeQuote, an AI-powered quoting and lead qualification platform for tree service companies, created from direct operator pain points and validated via an image-based remote quoting workflow. Has already built a targeted customer acquisition motion resulting in 5 active paying subscribers, and brings experience proposing AI-driven workflow automation to remove operational bottlenecks in a high-volume install environment.”

Taruni Reddy Ampojwala

Screened

Mid-level GenAI Engineer specializing in LLM agents and RAG systems

Brooklyn, NY4y exp

“Built and deployed a production RAG-based LLM assistant that answers day-to-day operational questions from internal PDFs/SOPs, with strong emphasis on data consistency (metadata versioning, confidence thresholds, conflict handling) and low-latency retrieval at scale. Experienced designing and orchestrating multi-agent LLM workflows (retrieval/validation/generation) and pipeline orchestration for ingestion/embedding/vector-store updates, plus iterative delivery with non-technical operations/business stakeholders.”

Jookwang Jung

Screened

Staff Software Engineer specializing in AI-native platforms and full-stack systems

Los Angeles, CA20y exp

“Founding engineering lead and player-coach who built a small cross-functional team around an AI-first development model, increasing execution speed by more than 3x through hands-on coaching and human-in-the-loop practices. Has led architecture for a multi-tenant platform and built an AI system for fashion businesses that generates content, imagery, and operational recommendations, combining technical depth with product and UX judgment.”

“Startup-focused full-stack engineer who has worked across fintech and digital health, including Pivotxy and Cybele Health. They combine backend/API development with AI integration, including GPT-powered financial reporting and a finance agent benchmark, and have helped turn manual report workflows that took weeks into outputs generated in minutes.”

“Built and shipped a production LLM-powered incident response agent for a microservices platform, automating alert triage and safe remediation recommendations with strong guardrails (RAG grounding, structured JSON outputs, rule-based validation, and human-in-the-loop). Implemented state-machine orchestration (Redis/Kafka), comprehensive eval/monitoring, and an error categorization pipeline that cut hallucination errors ~40% and reduced MTTR ~30%.”