Vetted Logistic Regression Professionals

“Analytics-focused candidate with hands-on experience in both supply chain data and AI product analytics. They have built SQL and Python pipelines for messy ERP/inventory data as well as high-volume user event data, and have driven experimentation, retention measurement, and dashboarding for AI avatar and voice/image cloning features.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

“Full-stack product engineer who has shipped customer-facing features end-to-end, including a product detail page backed by Java/Spring Boot microservices and a React/TypeScript UI. Demonstrated measurable impact through performance and maintainability improvements (30% faster APIs, 25% less duplicated UI code, 40% reduced API complexity via GraphQL) and has operated/scaled apps on AWS with CI/CD, monitoring, and incident-driven scaling fixes.”

“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”

“Data scientist at Guardian Airwaves building a RAG-powered quiz generator using Grok AI, with hands-on experience solving hard document-ingestion problems (PDFs with images/tables) via unstructured.io and LlamaIndex. Has deployed production systems on AWS EC2 and brings a pragmatic approach to agent reliability (human-in-the-loop, LLM-based eval, latency/cost metrics) while effectively translating RAG concepts to non-technical stakeholders.”

“Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.”

“Backend engineer at Esurgi focused on real-time clinical workflow systems, improving API reliability, performance, and security. Has hands-on experience with FastAPI/Pydantic, JWT/RBAC and row-level data isolation, plus Kafka-based real-time processing—including fixing duplicate-processing edge cases via idempotency and offset management and rolling out refactors safely with feature flags and staged deployments.”

Naisarg Halvadiya

Screened

Senior Full-Stack & AI Engineer specializing in scalable web platforms and LLM automation

Bengaluru, India4y exp

“Built a production agentic AI assistant in Python using Playwright plus Google Gemini’s vision capabilities to automatically document and execute UI workflows step-by-step, reducing developer time spent on trivial documentation/knowledge transfer. Also built an Apache Airflow ETL pipeline and has experience evaluating AI agents with human-in-the-loop methods, plus successfully communicated a vision-model-based CMS analytics PoC to non-technical university stakeholders and proposed it to Academic Technology with cost-savings rationale.”

“Senior AI/ML engineer with hands-on experience building production LLM systems in healthcare, including RAG-based clinical question answering and end-to-end MLOps on Vertex AI and Kubernetes. They combine strong platform engineering with applied GenAI work, citing a 35% improvement in factual accuracy and a 30% boost in internal team productivity through modular Python services and CI/CD.”

“Healthcare-focused ML/AI engineer who has built clinical note summarization and medical image annotation solutions using LLMs, RAG, and multimodal models. They combine experimentation across major model providers with practical production concerns like monitoring, drift detection, and latency/cost tradeoffs, and also earned 2nd place in a Google hackathon for a medical AI assistant.”

“Full-stack engineer with hands-on ownership of a production expense management platform built on Next.js, NestJS, PostgreSQL, and MongoDB. Stands out for solving real production scale issues—optimizing 2M+ record query performance, redesigning cross-database sync with an outbox/event-driven approach, and cutting API latency from 2-3 seconds to under 300 ms.”

“Built and deployed an agentic RAG platform at Centene Health to support healthcare claims and complaints workflows (Q&A for claims agents, executive complaint summarization, and compliance triage/classification). Experienced in LangChain/LangGraph orchestration, production deployment on AWS with FastAPI/Docker/Kubernetes, and implementing HIPAA-compliant guardrails to reduce hallucinations and ensure explainable outputs.”

“Built and productionized a clinical NLP + patient risk stratification platform at Dermanture, combining Spark/PySpark pipelines with BERT/BioBERT for entity extraction and text classification and downstream risk models in TensorFlow/scikit-learn. Experienced running regulated, auditable ML workflows with Airflow and AWS SageMaker, emphasizing data validation (Great Expectations), drift monitoring, and explainability (SHAP) to drive clinician trust and adoption.”