Vetted Large Language Models (LLMs) Professionals

“Candidate brings an operator-to-founder profile with leadership experience in IT and Business Systems and a strong grasp of how ideas become venture-backable products. They speak fluently about startup evaluation criteria such as TAM, technical defensibility, speed to scale, and AI differentiation, and appear especially motivated by building solutions end-to-end in startup or venture studio environments.”

“Paid social creative lead with experience across major consumer brands (Duracell, Amazon Groceries, Puma) and Gen Z-focused retail, owning end-to-end creative strategy from concept/briefing and UGC direction through QA and delivery. Known for contextual, seasonally-timed campaigns and performance-driven iteration (CPA/ROAS, funnel drop-offs), including a Duracell Meta campaign that reached 20M+ views and sustained engagement gains via creator feedback loops.”

“LLM/agentic workflow builder focused on productionizing document-processing systems. Redesigned pipelines with LangGraph + RAG, schema-aware validation, and eval/monitoring loops; known for fast incident diagnosis (restored accuracy from ~70% to >95% same day). Partners closely with sales and stakeholders to deliver tailored demos and drive adoption (reported +40%).”

“At Triple Whale, partnered with product, engineering, and sales to bring enterprise LLM-based budget recommendation agents from impressive prototypes to trusted production workflows. Strong in prompt/input tuning, explainable structured outputs, and running tightly-scoped POCs with clear success criteria—plus hands-on technical demos and post-sale implementation to drive adoption.”

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“JP Morgan engineer and Texas A&M student web developer who has owned production systems end-to-end, including a real-time ML training workflow that improved internal search relevance by 30%. Experienced with AWS cloud migrations and operating containerized services on ECS with CloudWatch+ELK observability, Terraform infra, and Spinnaker CI/CD; also built event-driven pipelines with RabbitMQ and Elasticsearch at 1M+ record scale.”

Prateeksha Ranjan

Screened

Mid-level Software Engineer specializing in embedded AI and full-stack systems

Irvine, California4y exp

“Robotics software engineer who built and owned core navigation components for a TurtleBot in ROS/ROS2 and Gazebo, including an RRT-based planner, waypoint-to-velocity motion planning, and PID trajectory tracking. Demonstrates strong real-time debugging skills (control-loop timing under CPU load), costmap/occupancy-grid tuning, and distributed ROS2 communication design using DDS/QoS, plus Docker and CI/CD automation experience from Keysight.”

Prateek Patil

Screened

Engineering Leader specializing in Digital Health, AI, and Cloud Platforms

Santa Clara, CA16y exp

“Senior Engineering Manager at Roche leading two Scrum teams building internally shared (“inner-sourced”) tools and libraries for a healthcare enterprise. Has led security/compliance-first architecture decisions (e.g., Python AI modules running inside a Java container) and front-end modularization (Angular monorepo to module federation), with a strong focus on developer experience via automated Swagger/OpenAPI documentation and robust testing/versioning practices.”

Advitha Bawgi

Screened

Junior Full-Stack Software Engineer specializing in cloud-native microservices

India1y exp

“Backend engineer with hands-on IoT and AI product work: built a decoupled Raspberry Pi + AWS IoT Core weather monitoring backend and a Dockerized FastAPI LLM service on AWS ECS using OpenAI/HuggingFace with an emerging RAG layer. Also delivered measurable performance gains at DAZN by redesigning event-driven/serverless ingestion (SNS, S3->Lambda->DynamoDB), cutting latency ~30% and boosting throughput ~25% while automating ~90% of manual sync work.”

Abhishek Adinarayanappa

Screened

Junior Software Engineer specializing in backend, cloud, and machine learning systems

Miami, FL3y exp

“Built Digipulse, a university project that ingested and clustered Bluesky tweet data at scale and used Gemini to generate near-real-time topic summaries, processing 1M+ tweets per day. Also brings Intel experience with Prometheus and Kubernetes, including production monitoring and incident troubleshooting.”

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

Aarushi Mahajan

Screened

Mid-level AI/ML Engineer specializing in NLP, Generative AI, and MLOps

New York, USA4y exp

“Internship experience shipping production AI systems: built an end-to-end RAG platform (Python/FastAPI + LangChain/LangGraph + vector search) to answer support questions from unstructured internal docs, with a strong focus on hallucination prevention through confidence gating and rigorous offline/online evaluation. Also delivered an AI-driven personalization/analytics feature using an unsupervised clustering pipeline, iterating with PMs to align statistically strong clusters with actionable business segmentation.”

Ming Wang

Screened

Entry-level Software Engineer specializing in AI and FinTech

Hong Kong, Hong Kong1y exp

“Recent college graduate and software engineer who relies heavily on AI-assisted development, reporting that roughly 85% of code in a recent initiative was AI-generated and then manually reviewed. Has built customer-facing AI features including personalized recommendations and an internship chatbot tied to product advertising, with exposure to API communication, database checks, and conversation monitoring.”

“Backend engineer with hands-on experience building event-driven Java/Spring Boot and Kafka systems, plus AI-assisted document-classification workflows in enterprise environments. Stands out for a thoughtful, risk-aware approach to AI: uses it to accelerate delivery, but emphasizes validation layers, confidence thresholds, observability, and human review before AI can affect downstream business actions.”

“Senior engineering executive who has spent the last decade leading large-scale platform modernization while staying deeply hands-on in architecture, debugging, and code reviews. He has led 120+ person organizations, built cloud-native SaaS and identity platforms across regulated and enterprise domains, and ties engineering decisions directly to revenue outcomes, including contributing to Wind River's $1B+ milestone.”

“Player-coach engineering leader currently running a Solution Architecture/FDE team responsible for both presales and postsales delivery. Stands out for combining enterprise systems thinking with hands-on AI product work: they built configurable tooling that sped delivery by ~30%, drove a Kafka-to-Pulsar architecture shift for scale, and spent the last two years building LLM-based document extraction and RAG inference pipelines shaped directly by user feedback.”