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Vetted Model Evaluation Professionals
Pre-screened and vetted.
VinaykumarReddy Junuthula
Junior Software Engineer specializing in backend APIs and ML-driven systems
“Internship experience at Paycom owning an end-to-end personalized course recommendation feature for an LMS, spanning SQL-based data pipelines, ML integration, and FastAPI REST services for real-time recommendations. Focused on production tradeoffs (latency vs. accuracy), scaling/SQL optimization, and post-launch iteration driven by engagement metrics.”
Harsh Chauhan
Junior AI Engineer specializing in Generative AI, RAG, and NLP
“AI/LLM engineer who has shipped a production RAG platform at Ticker Inc. on GCP (Qdrant + Postgres) delivering sub-second retrieval over 550k+ items, with measurable gains in latency and answer quality (HNSW optimization, MMR re-ranking). Also built an asynchronous LangChain/LangGraph multi-agent research system (10x faster cycles) and partnered with Indiana University doctors on synthetic patient records and ML error analysis using clinician-friendly F1/loss dashboards.”
Jayasri Guthula
Mid-level Applied ML Engineer specializing in LLM evaluation and multimodal agent systems
“Full-stack engineer working at the intersection of product and infrastructure, building developer-facing interfaces for AI voice agents in XR/immersive environments plus telemetry-heavy analytics dashboards. Experienced in Postgres telemetry data modeling and performance tuning, and in designing durable multi-step LLM pipelines with idempotency, retries, and strong observability; has operated in fast-moving startup-like teams (Biocom, HandshakeAI).”
Hari Krishna Kona
Mid-level AI/ML Engineer specializing in Generative AI and LLM-powered NLP
“LLM/AI engineer who built a production automated document-understanding pipeline on Azure using a grounded RAG layer, designed to reduce manual review time for unstructured financial documents. Demonstrates strong real-world scaling and reliability practices (Service Bus queueing, Kubernetes autoscaling, observability, retries/circuit breakers) plus rigorous evaluation (shadow testing, replaying traffic, multilingual edge-case suites) and stakeholder-friendly, evidence-based explainability.”
Will Hu
Intern-level Software Engineer specializing in machine learning and backend systems
“Software development intern who owned and shipped a production-used WeChat mini-program (JavaScript + MongoDB) serving ~3,000 users in a semester. Emphasizes maintainable UI architecture through modular, reusable components and clear separation between UI presentation and data/business logic, with a performance mindset (caching/reducing redundant updates).”
Sreshta Reddy Basi Reddy
Entry-level Software Engineer specializing in systems, data, and full-stack development
“Built a production-style hackathon prototype for analyzing healthcare facility data and identifying medical deserts via natural-language queries. Stands out for a pragmatic applied-AI approach: separating retrieval from LLM reasoning, using structured JSON outputs, and designing fallbacks and data-quality checks to keep recommendations grounded and reliable.”
Darion Jefferson
Mid-level IT & Cloud Security Specialist specializing in GRC, SOC workflows, and agentic AI automation
“Builder/creator who ships practical AI automations and content workflows: created a no-backend website that uses ChatGPT to generate AI agents/manual workflows, and built an inbound/outbound receptionist using n8n and Retell AI (later migrated to Retell workflows). Also produces an AI-written/produced podcast with 55+ hosts and uses tools like Descript and Sora with make.com for batch content creation and scheduling.”
chandankumar ramamurthy
Junior Full-Stack Engineer specializing in LLM-powered products
“Built multiple systems from scratch at DSSD and Aglint, including an NGO sustainability reporting dashboard and a production LLM-powered phone screening agent using Twilio/Retell AI with RAG grounded in PostgreSQL candidate/job data. Strong focus on real-world reliability: guardrails, monitoring, and lightweight eval/regression loops that reduced recruiter score overrides by ~30%. Currently on OPT through May 2026 (plans STEM OPT extension) and committed to relocating to NYC for in-person work; seeking $90k–$120k base with meaningful equity for founding engineer roles.”
Keeravani Chekuri
Mid-level AI/ML Engineer specializing in LLM systems and MLOps
“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.”
“Full-stack AI engineer focused on operational and healthcare analytics use cases, with hands-on experience building React/TypeScript frontends and Node/FastAPI/Flask backends for agentic systems. Stands out for combining LLM orchestration, retrieval grounding, and human-in-the-loop controls with measurable business impact, including a fraud detection dashboard that achieved 92% accuracy and cut manual review time by 85%.”
Samhitha Muvva
Mid-level Full-Stack AI Engineer specializing in agentic systems
“At ReferU.AI, designed and deployed an agentic RAG pipeline that automates multi-jurisdiction legal document drafting, emphasizing hallucination reduction through hybrid retrieval, validation agents, guardrails, and iterative regeneration. Experienced with orchestration frameworks (especially CrewAI) and rigorous testing/evaluation practices including human-in-the-loop review, adversarial testing, and production metrics/logging.”
Arpitha Nagaraj
Junior Software Engineer specializing in backend systems and full-stack development
“Full-stack software engineer with hands-on experience shipping AI-driven product experiences, including a conversational travel planner and a RAG-based PDF question-answering system. Has also built enterprise automation APIs at Accenture for network diagnostics, combining backend engineering, testing automation, and user-focused product simplification for non-technical operations teams.”
Alex Lopez
Executive AI product and platform leader specializing in FinTech
“Founder and CTO of Relish who scaled the company through Series A and B, raising over $35M, while building an enterprise AI invoice-processing platform for major customers including Apple and PayPal. Combines deep technical fluency in LLM/OCR orchestration and ERP integrations with strong product and UX judgment, including a redesign that improved NPS by roughly 20 points. Also brings a university teaching background that informs a distinctly human-centered view of AI.”
Atharva Jagtap
Junior Full-Stack Software Engineer specializing in cloud and AI/ML applications
“Full-stack engineer with hands-on experience across e-commerce personalization, enterprise RAG assistants, and cloud infrastructure automation. They’ve shipped AI features using Azure LLM APIs and vector search, improved recommendation engagement, and worked across frontend, backend, ML-informed analytics, and AWS infrastructure in early-stage environments.”
Henry Li
Junior Software Engineer specializing in mobile, full-stack, and game development
“Engineer with hands-on experience shipping production-critical AI-assisted workflows, including an internationalization system for a React Native/Expo mobile game that expanded from English-only to 15 languages. Stands out for pragmatic, safety-first use of AI: they design deterministic validation layers, constrain model scope, and prioritize architecture, QA, and maintainability over hype-driven agent complexity.”
Krutarth Chaudhari
Mid-level Software Engineer specializing in full-stack web and AI systems
“Full-stack and AI systems engineer who has shipped both enterprise fintech and real-time voice AI products. At NCR, they repaired a broken transaction-fee workflow end to end using React/TypeScript, Lambda, and DynamoDB; at Lillup, they turned a research voice prototype into production infrastructure, cutting latency from 350ms to under 200ms and raising user ratings from 2.8 to 4.1. They also built an LLM side project that uses Claude and GPT to cross-review each other through structured JSON consensus rounds.”
Krishna vamsi Dhulipalla
Mid-level Software & ML Engineer specializing in agentic LLM systems and ML infrastructure
“Built and deployed an LLM-to-SQL automation system in a closed/internal environment, using a retriever–reranker–validator architecture on Kubernetes with strong security controls (semantic + rule-based validation and RBAC), achieving 99% uptime and cutting manual query time ~40%. Also worked on genomic sequence classification and semantic search workflows, orchestrating data prep with Airflow, tracking/deploying with MLflow, and optimizing distributed multi-GPU training on a university Kubernetes cluster.”
Lahari Chamarthi
Mid-level Data Scientist specializing in NLP, recommender systems, and ML deployment
“At Provenbase, built and shipped a production LLM-powered semantic search and candidate matching platform (RAG with GPT-4/Gemini, multi-agent orchestration, Elasticsearch vector search) to scale sourcing across 10M+ candidate records and 1000+ data sources. Drove sub-second performance, cut LLM spend 30% with routing/caching, and improved recruiting outcomes (+45% sourcing accuracy; +38% visibility of underrepresented talent) through bias-aware ranking and tight collaboration with recruiting stakeholders.”
Meghana Chowdary Borra
Junior Machine Learning Engineer specializing in predictive modeling and GenAI RAG systems
“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
Junior Machine Learning Engineer specializing in multimodal systems and LLMs
“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.”
Aneri Patel
Junior Machine Learning Engineer specializing in LLM fine-tuning and semantic retrieval
“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”
Yanet Fernandez
Junior Data Analyst specializing in healthcare analytics
“Analytics/data professional with hands-on experience turning messy semi-structured CRM JSON data in Snowflake into clean reporting layers using SQL and validation logic. Brings a practical mix of data engineering, Python automation, metric design, and stakeholder alignment to improve reporting accuracy and speed of decision-making.”
Rohit Viswakarma Pidishetti
Mid-level Full-Stack Engineer specializing in real-time frontend systems
“Frontend-leaning full-stack engineer who built and largely owned YAARI, a browser-based social media platform with real-time interactions, media handling, and AI-based content validation. Stands out for combining React performance tuning, real-time UX design, and security-conscious backend integration in a complex consumer-style application.”
Jonathan Lee
Director-level Engineering Leader specializing in agentic AI systems
“Engineering leader and hands-on architect who built a team from scratch and owned everything from architecture and security to DevOps and deployment. Has led sophisticated AI/agentic systems in legal-tech and operations, including demand-letter automation, news/content generation, and human-in-the-loop fax routing, while also guiding major infrastructure and enterprise telephony transitions.”