Vetted Streamlit Professionals

“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”

“Founding AI engineer at an AI SaaS startup who built the full GTM knowledge and retrieval stack for non-technical teams, driving 60% less manual effort and 25% faster deployments. Also brings enterprise B2B SaaS integration experience from Wipro, including external API/documentation work for large-scale partner ecosystems.”

Likitha Kukunarapu

Screened References

Mid-level Applied AI Engineer specializing in data engineering and healthcare AI

Remote, USA3y exp

“Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.”

Preetham Dandu

Screened ReferencesModerate rec.

Junior Software Engineer specializing in backend, distributed systems, and cloud platforms

Stony Brook, NY1y exp

“MS candidate with strong backend/data engineering focus who builds research and data systems with production-grade rigor (reproducibility, observability, restartability). Has hands-on experience securing and scaling FastAPI-based gateways in front of Java microservices, leading SQL Server→Snowflake migrations with dual-write/feature-flag rollouts, and hardening Kafka-based fleet-tracking systems against out-of-order and duplicate events.”

Laxminarayana Yaga

Screened

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

Missouri, USA4y exp

“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”

Siva Pothuru

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and cloud-native ML

San Antonio, TX5y exp

“LLM/agent engineer at USAA who built a production GPT-4o RAG conversational assistant for financial analysts, focused on regulatory interpretation and internal documentation search. Emphasizes compliance-grade reliability with strict grounding, safe fallbacks, and full auditability via MLflow/DVC plus human-in-the-loop review; reports ~45% reduction in ticket resolution time.”

“Built and owned an enterprise AI document-processing deployment at an automotive tech startup, taking it from discovery to stabilization. Strong in production LLM/RAG systems and backend reliability, with measurable impact including 8,000+ documents processed monthly and turnaround time reduced from nearly 24 hours to about 3 hours.”

“Backend engineer who built and evolved the real-time networking/messaging backend for a cross-platform professional networking app (Make Connexions), optimizing for low-latency delivery, privacy, and strong consistency. Experienced scaling Python/FastAPI APIs with async + Redis, and leading safe refactors via versioned endpoints, feature flags, and backward-compatible migrations; strong production auth/RLS expertise including refresh-token rotation edge cases.”

“Built and deployed a production LLM-powered support assistant for customer support agents using a RAG architecture over internal docs and past tickets, with human-in-the-loop review. Demonstrates strong applied LLM engineering focused on real-world constraints (hallucinations, latency, cost) using routing to smaller models, reranking, caching, and rigorous evaluation/monitoring (offline eval sets, A/B tests, KPI tracking).”

“Cloud/ML engineer with production deployment experience on Azure (Dockerized models, managed APIs, data pipelines) who has repeatedly stabilized unreliable systems—e.g., taking an API-driven analytics pipeline from ~60% to 98% reliability and an Azure ML service from ~80% to 97% by addressing rate limits, container memory, and gateway timeouts. Also built an explainable contract-risk model for entertainment bookings (Transformers + SHAP) and integrated it into a legacy booking system via a Flask REST API, plus prior IoT work at Nissan processing CAN bus sensor streams for diagnostics/anomaly insights.”

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”

“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”

Chin-yu Wu

Screened

Junior Data Analyst specializing in sports analytics and business intelligence

Indianapolis, IN2y exp

“Analytics professional in the sports industry who has owned high-impact revenue and compliance data projects for the Colts, turning fragmented Ticketmaster and Salesforce data into trusted real-time reporting. Stands out for combining strong SQL/Snowflake engineering, rigorous validation practices, and stakeholder-facing metric design that drove a record 98% compliance rate and meaningful revenue recovery.”

“Engineer with hands-on experience owning customer deployments for ordering and billing systems at Amdocs, including performance tuning, CI/CD improvements, and post-launch stabilization that delivered about 50% faster execution time. Also built and debugged an LLM-powered task prioritization app using Gemini, Streamlit, Python, and MongoDB, with a strong focus on prompt reliability, validation, and handling inconsistent real-world inputs.”

“AI/backend engineer who has built multiple applied AI systems end-to-end, including an underwriting document intelligence copilot, ambient clinical documentation workflows, and a financial analysis agent. Stands out for combining practical LLM architecture choices with reliability mechanisms like human-in-the-loop review, eval frameworks, and grounded retrieval in production settings.”