Vetted Streamlit Professionals

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

“Embedded/real-time robotics-style engineer with hands-on STM32 development, sensor integration, and low-level drivers, focused on deterministic control behavior. Demonstrated systematic debugging of jitter/latency by instrumenting the sensing-to-actuation pipeline and eliminating blocking via interrupts, hardware timers, and DMA; also designs asynchronous, message-based interfaces for distributed real-time components. Familiar with ROS/ROS2 concepts (nodes/topics/callbacks) though not yet deployed a full production ROS system.”

“Developed and productionized an AI-driven "indication finding" system for AbbVie to identify additional diseases a drug could target, working closely with clinical research teams on cohort inclusion/exclusion criteria and disease rollups. Leveraged an LLM to map clinical inputs to ICD codes and built configuration-driven ML pipelines (Cloudera ML, YAML, scheduled jobs) with structured testing and evaluation for reliability.”

“Built and deployed a production LLM-powered multi-agent compliance copilot (life sciences/finance) using LangChain/LangGraph + RAG over vector databases, delivered via async FastAPI on Kubernetes. Emphasizes audit-ready, deterministic outputs with schema constraints and citations, plus rigorous evaluation/monitoring; reports 60%+ reduction in manual research time and successful production adoption.”

“Backend-focused engineer who built a resume processing and job application platform using Python/MongoDB/Streamlit, including OpenAI-powered skill/keyword extraction and recruiter-facing search/filtering. Has hands-on cloud deployment experience on AWS/Azure and executed an on-prem reservation portal migration to Azure using a phased trial-and-cutover approach; also automated CI/CD with Jenkins and GitHub Actions.”

“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.”

“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”

“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).”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“LLM engineer who built a production seller-support RAG system at eBay using hybrid retrieval (BM25 + Pinecone vectors) with Cohere reranking, LangGraph orchestration, and citation-grounded answers. Strong focus on reliability: semantic/structure-aware chunking, automated Ragas-based evaluation with nightly regressions, and production observability (LangSmith) plus drift monitoring (Arize). Also implemented a multi-agent fraud pipeline with AutoGen using JSON-schema contracts and explicit termination conditions.”

Anvith Reddy Dodda

Screened

Mid-level AI Engineer specializing in GenAI, NLP, and MLOps

Remote, USA3y exp

“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”

“ML/LLM infrastructure engineer who built and shipped a production internal evaluation + failure-analysis agent (Arthur AI / R3AI context) that orchestrated end-to-end benchmarks with deterministic lineage, regression detection, and root-cause reporting at 5,000+ benchmarks/week. Also built backend observability and data validation systems for analytics pipelines at FullStory processing ~3.4B weekly events, emphasizing schema validation, quarantine fallbacks, and idempotent operations.”

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.”