Vetted AI & Machine Learning Professionals in the Greater Phoenix

“Built and owned end-to-end production systems for a healthcare platform, including a predictive task recommendation feature (React + FastAPI + ML on AWS ECS) that cut backlog 20% and saved coordinators ~10 hours/week. Also productionized an AI-native RAG system (vector DB + LLM) delivering 40% faster query resolution, and led phased modernization of a monolithic FastAPI service into async microservices using feature flags and canary releases.”

“Built and productionized a Python/LLM-based pipeline at Catalyst Solutions to automate healthcare RFP processing, turning unstructured documents into validated JSON/Excel with schema validation, confidence scoring, and human-review routing. Delivered major operational impact (hours-to-minutes processing, ~60% efficiency gain; 50+ RFPs processed) and modernized legacy scripts into a staged, more reliable architecture using incremental refactoring and fallback comparisons.”

“Currently working in CoRAL Lab, built and deployed IntegrityShield—a document-layer PDF watermarking system that keeps assessments visually identical while disrupting LLM-based solving; validated in a real classroom where it helped catch 12 AI-cheating cases. Also built MALDOC, a modular red-teaming platform for document-processing AI agents using LangGraph to run reproducible, deterministic adversarial trials across OCR/text/vision routes.”

Sri Teja

Screened

Mid-level AI Engineer specializing in LLM systems and enterprise data platforms

Phoenix, AZ5y exp

“Built and owned key parts of Ripley, an AI-powered multi-agent operations platform for roadside assistance that automates high-volume customer service workflows at production scale. They designed the orchestration, evaluation, monitoring, and enterprise integrations, helping drive 70-80% automation and ~99% reliability across thousands of weekly interactions and millions of annual requests.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“Full-stack product builder with hands-on experience improving onboarding and reducing churn through guided tours, instrumentation, and A/B-tested feedback loops. They’ve also prototyped AI systems including a text-to-SQL RAG-based multi-agent workflow and built a real-time multiplayer React/TypeScript app on Supabase, while showing strong instincts around evaluation, UX, and production trade-offs.”

“AI/LLM engineer who has shipped production RAG chatbots using LangChain/OpenAI with FAISS and FastAPI, focusing on real-world constraints like context windows, concurrency, and latency (reported ~40% latency reduction and <2s average response). Experienced orchestrating AI pipelines with Celery and fault-tolerant long-running workflows with Temporal, and has applied NLP model tradeoff testing (Word2Vec vs BERT) to drive measurable accuracy gains.”

“Full-stack and AI systems engineer who has built across ride-hailing, fintech, higher-ed support, and legal-tech workflows. Stands out for shipping production RAG/agent systems with careful grounding and human fallback, while also delivering hard backend architecture wins like geospatial dispatch scaling and cutting fintech payment latency from 60 seconds to 2 seconds.”

“Built and shipped a personal LLM-powered news aggregation platform (Clear Brief) that scrapes ~200 articles per cycle, clusters them into ~15–30 consolidated stories, and supports on-demand deep dives via a Next.js API route. Emphasizes production-minded reliability (token/cost controls, timeouts, graceful frontend degradation) and database-backed orchestration using SQLite with retry + exponential backoff for burst processing.”