Vetted Large Language Models (LLMs) Professionals

“ML engineer with hands-on experience building banking AI systems end-to-end, including a customer-targeting model that improved campaign response rates by about 10%. Also shipped a RAG-based banking FAQ/support feature with safety guardrails and production optimizations around retrieval quality, latency, and cost, plus reusable Python services that reduced duplicate work for other engineers.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“Python backend engineer (BetterWorld Technology) who owns microservice systems end-to-end on Azure, including Kubernetes deployments, CI/CD, and production monitoring/alerting. Has hands-on experience integrating SQL/NoSQL (including Cosmos DB with vector search/graph workflow) and has built a Kafka + Spark Streaming pipeline to Snowflake with a reported 40% latency reduction.”

“Applied AI/ML engineer with hands-on production experience building a RAG-based AI assistant for pharmaceutical maintenance troubleshooting using LangChain + FAISS/Pinecone, including a custom normalization layer to handle inconsistent terminology and duplicate document revisions. Also built Airflow-orchestrated pipelines for document ingestion/embeddings and predictive maintenance workflows (SCADA ETL, drift-based retraining), and partnered closely with production supervisors/quality engineers via Power BI dashboards and real-time alerts.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

Moore Macauley

Screened

Intern Backend Developer specializing in AI, multi-agent systems, and computer vision

0y exp

“Backend-focused Python engineer who built core systems for an AI beauty-advice product: converting facial-recognition landmarks into usable facial measurements and dynamically shaping chatbot context for personalized guidance. Also worked on high-volume data ingestion at AINVESTgroup, improving agent context selection via a RAG database when upstream tags were unreliable, and has strong Git/GitOps + automated testing practices from rapid-deadline delivery environments.”

Srinivasan Gomadam Ramesh

Screened

Mid-level AI/Data Engineer specializing in agentic AI and data platforms

Redmond, WA7y exp

“AI/LLM engineer who built a production resume-parsing and candidate-matching platform at Quadrant Technologies, combining agentic LangChain workflows, VLM-based document template extraction (~85% accuracy), and a hybrid RAG backend for resume-to-JD search. Notably integrated automated LLM evals and metric-based CI/CD quality gates to catch silent prompt/model regressions, and led a 3-person team across frontend/backend/testing.”

“Built a production lead-generation system using AI agents that researches the internet for relevant leads and integrates RAG-based contact enrichment/shortlisting aligned to existing CRM data, enabling sales reps to focus more on selling. Also has hands-on AWS data orchestration experience (Glue, Step Functions) moving raw data into Redshift and evaluates agent performance with human-in-the-loop plus BLEU/perplexity metrics.”

Nidhi Sura

Screened

Junior Software Engineer specializing in AI platforms, distributed systems, and cloud infrastructure

Dublin, CA1y exp

“Software engineer with limited robotics background but deep experience building end-to-end document ingestion and image understanding systems, including a CAD-specific pipeline using a custom model to extract components and bounding boxes for user-facing visualization and Q&A. Also brings strong infrastructure/DevOps skills (Docker, Kubernetes, GitHub Actions, Terraform) with emphasis on reliability, cost optimization, and uptime.”

Ashay Panchal

Screened

Mid-level Software Engineer specializing in AI-driven distributed systems

San Jose, CA4y exp

“Backend engineer who built a high-stakes, privacy-first platform at be Still Analytics for survivors of domestic violence, emphasizing anonymity, security, and reliability. Experienced with GenAI backends (LangChain + AWS Bedrock) including RAG to prevent hallucinations, plus cloud-native scaling (Docker/Kubernetes) and cost-saving migrations from legacy VMs to serverless (30% reduction).”

Kundhana Paruchuru

Screened

Mid-level Data Scientist specializing in ML, LLM pipelines, and MLOps

Remote, USA3y exp

“Built and deployed a production LLM-driven document understanding pipeline using LangChain/LangGraph, focusing on reliability via step-by-step prompting, validation checks, and monitoring. Also partnered with non-technical marketing stakeholders at Heartland Community Network to deliver an XGBoost targeting model surfaced in Power BI, improving campaign conversion by 12%.”

“Candidate is targeting a CTO Venture Studio role and positions themself as a technical partner to founders rather than a founder personally. They demonstrate strong fluency in early-stage startup evaluation, especially around validating whether a product truly tests the business hypothesis and whether the underlying technology can scale significantly.”

Keeravani Chekuri

Screened

Mid-level AI/ML Engineer specializing in LLM systems and MLOps

Boston, MA3y exp

“Built and deployed an AI tutoring assistant end-to-end at Nexora School, spanning discovery with school districts, multi-agent LangGraph/RAG architecture, AWS Bedrock migration, and post-launch stabilization. Stands out for combining hands-on LLM systems engineering with strong educator-facing trust building, FERPA-driven architecture decisions, and disciplined production practices around evals, logging, and messy document ingestion.”

“AI/automation-focused implementation engineer who has owned customer-facing LLM deployments end-to-end, spanning support automation, lead outreach, and messy document-processing workflows. Stands out for combining hands-on technical depth in Python/OpenAI/RAG systems with measurable business impact, including cutting support resolution time from 24 hours to 6 hours and reducing manual outreach work by 60%.”

“Built and deployed production NLP/LLM systems including a multilingual (5-language) health misinformation detection pipeline with latency optimization (batching/quantization/caching) and explainability (gradient-based attention visualizations). Experienced orchestrating end-to-end AI workflows with Airflow and Prefect, and partnering with customer support ops to deliver an AI agent for ticket summarization and priority classification with clear, measurable acceptance criteria.”

“Frontend product builder who has shipped and maintained a two-mobile-app ecosystem (user + employee) backed by Node.js, emphasizing separation of concerns, shared libraries for reuse, and TypeScript type safety. Re-architected a Sunmor Research codebase using MVC, improving readability and collaboration and taking the product from unusable to working, with a strong regression-testing mindset and customer-feedback-driven iteration.”

Meghana Chowdary Borra

Screened

Junior Machine Learning Engineer specializing in predictive modeling and GenAI RAG systems

Buffalo, New York2y exp

“LLM engineer who built and deployed an emotionally intelligent AAC communication system using an emotion-aware RAG pipeline (Empathetic Dialogues + GoEmotions) and a PEFT-adapted model. Experienced with LangChain/LangGraph and custom Python orchestration, focusing on reliability (guards, schema validation, fallbacks), latency optimization, and rigorous evaluation (automatic metrics + human-in-the-loop), with a reported 18% user satisfaction improvement.”

Krishna K

Screened

Junior Machine Learning Engineer specializing in multimodal systems and LLMs

Jersey City, NJ2y exp

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”

“Robotics/AV-focused candidate who contributed to an F1TENTH autonomous vehicle college project, building key autonomy components from raw sensor data to driving commands. Strong in perception and state estimation (visual odometry, particle-filter localization), plus mapping (occupancy grids) and planning/control (RRT, Gap Follow, PID), with hands-on ROS tooling and simulation validation in Gazebo/RViz and ROS environment containerization using Docker.”

“JavaScript engineer with open-source experience on a database visualization library, focused on real-time rendering performance for large datasets (virtualized DOM rendering, requestAnimationFrame/debouncing, memoization) and on raising project quality via tests and CI performance benchmarks. Also built Kafka-based messaging documentation and sample producer/consumer apps to speed onboarding, and has experience diagnosing production issues including concurrency-related duplicate data problems.”

Priyansh Aggarwal

Screened

Junior Software Engineer specializing in AI/ML and full-stack web development

Panchkula, India2y exp

“Built core perception and decision layers for a 3D AI-powered interactive avatar/agent with a robotics-like perception–reasoning–action loop, combining computer vision, NLP, and real-time response. Focused on making multimodal inputs robust (normalization, intent + emotion signal fusion) and improving real-time performance via instrumentation, profiling, and parallelization; also designed distributed, loosely coupled state-based communication and deployed services with Docker.”

Tao Chen

Screened

Senior Full-Stack Engineer specializing in AI-powered web products

Mountain View, CA7y exp

“Backend/data engineer who has built production AI video generation services on AWS using a hybrid serverless + container architecture (FastAPI, Lambda, ECS, Postgres/Redis) with strong reliability practices (auth, retries/timeouts, structured logging, CloudWatch + Slack alerting). Also delivered AWS Glue ETL pipelines with schema evolution handling and modernized a legacy SAS healthcare reporting workflow to Python with parity validation and parallel-run migration.”

Tamanna Nandlal Choithani

Screened

Entry-level Full-Stack Engineer specializing in AI and distributed systems

California, USA1y exp

“Full-stack engineer who built an AI-based inventory/procurement query system at Botlily/Botlerly using Flask and Google Sheets as a live knowledge base, overcoming Sheets latency with caching and structured in-memory models. Demonstrated strong LLM product engineering (40% accuracy improvement via preprocessing/prompting) and customer-driven iteration with bar/restaurant owners, evolving the tool into a more comprehensive inventory management and forecasting solution.”

“Senior engineering leader who says he spent the last 7 years implementing rather than planning, including building a one-person engineering function into a fully operational department with standards, KPIs, Scrum, QA, and automation. He also designed the full technical ecosystem across application, backend, enterprise architecture, ETL, IoT, and CI/CD, and has additional exposure to VC-backed environments and M&A integration leadership.”