Vetted Data Engineering Professionals

Rishitha Madipelli

Screened

Mid-level Software Engineer specializing in cloud-native distributed systems and streaming data

Austin, TX7y exp

“Backend/product engineer with Tesla experience building and operating a real-time OTA update monitoring and fleet analytics platform at massive scale (telemetry from 3M+ vehicles). Delivered end-to-end systems across Kafka-based ingestion, TimescaleDB/Postgres analytics modeling, FastAPI/GraphQL APIs, and React/TypeScript dashboards, and handled production scaling incidents on AWS EKS during major rollout spikes.”

“Healthcare data platform builder with experience at Aetion delivering a rule-based EMR/EHR ingestion and validation framework that cut onboarding from 8–10 weeks to hours and unlocked $30M+ in revenue over ~3 years. Motivated to found an AI/agent-driven healthcare solution, with a specific interest in using PET scans, doctor notes, and treatment data with LLMs to help predict cancer progression and guide next-step treatments.”

“AI/LLM engineer at Capital One who deployed a production RAG-powered fraud analysis and document intelligence platform using LangChain, OpenAI, Pinecone, Kafka, and AWS. Focused on reliability in real-time investigations via hybrid retrieval, schema-validated outputs, and LLM verification loops, reporting review-time reduction from hours to minutes and ~99% fraud detection precision.”

“Built and productionized a RAG-based analytics Q&A assistant for a financial analytics team, enabling natural-language querying across 200+ datasets (SQL tables, PDFs, compliance docs, wikis) and cutting turnaround time by 60%. Deep experience delivering regulated, audit-ready LLM systems on Azure (Azure OpenAI + LangChain) with strict grounding/citations, hybrid retrieval, and AKS-based low-latency deployment, plus strong collaboration with compliance analysts and auditors via iterative Gradio demos.”

“Built and deployed a production RAG-based multi-agent chatbot during an internship to help consultants answer client questions and guide users through new IT systems with step-by-step instructions. Demonstrates hands-on experience with LangGraph/LangChain/Google ADK, unstructured document parsing and chunking for RAG, and a reliability-first approach to agent workflows (metrics, fallbacks, human-in-the-loop, guardrails).”

“ML/LLM engineer building production, real-time fraud detection for financial transactions using a two-tier architecture (fast ML + GPT) to deliver both low-latency decisions and analyst-friendly risk explanations. Experienced orchestrating end-to-end retraining, drift monitoring, and automated model promotion with Databricks Jobs/Workflows and MLflow, and partnering closely with fraud analysts to tune alerts, thresholds, and dashboards.”

“Engineering leader who drove the conception, PRD, architecture, and delivery of MaxCDN’s next-generation CDN platform ("E2"), including control plane work, hardware deployment planning, and observability/billing data processing. Also built Krypton Labs’ engineering team from the first hires, using a flat Agile structure and emphasizing constructive conflict, strong documentation, and remote-team accountability.”

“Data/Backend engineer with experience at Naukri building large-scale analytics products over a 130M+ user base, including Spark/Airflow pipelines and Kafka-based clickstream validation with Confluent Schema Registry. Also built an audience segmentation backend (Athena/S3 + Spring Boot APIs) for non-technical internal teams and recently shipped a GenAI customer data audit system (FastAPI/Postgres/Llama) that cut sales-planning validation from ~3 months to ~1 week.”

Shruti Krishnagiri

Screened

Executive Engineering Leader & Technical Founder specializing in AI automation platforms

San Francisco Bay Area, California20y exp

“Founder/CTO who built and shipped a consumer subscription-bundling platform end-to-end (architecture, implementation, testing) and scaled it to thousands of customers and major partners. Previously led a major reliability overhaul at Chan Zuckerberg Initiative for a Google-Docs-like ed-tech product—boosted observability, introduced incident management, and migrated to a Docker-based scalable architecture. Heavy user of AI tools (Cursor/Claude) for development, testing, and code review, with a strong bias toward lightweight, fast-moving execution.”

Atulya Bist

Screened

Junior Data Scientist / Software Engineer specializing in LLM analytics and robotics

Los Angeles, CA3y exp

“Robotics/ML engineer who implemented TD3 and PPO in PyTorch to solve the challenging OpenAI Gymnasium humanoid-v5 MuJoCo task, including custom networks, rollout logic, and training scripts. Also has hands-on robotics coursework experience with ROS-based RRT motion planning on a real robotic arm, plus practical CI/CD and containerization experience (Docker, Jenkins, GitHub Actions). Currently exploring world models (VAE + sequence generator) using Euro Truck Simulator data.”

“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 a production B2C behavioral interview system for job seekers using LangGraph/LangChain on AWS Bedrock with Nova models, plus a FastAPI backend and Vercel AI SDK frontend. Stands out for practical agent reliability work: local stress testing, OpenTelemetry-to-Datadog observability, token/cost monitoring, and guardrails to keep conversations on track and resistant to instruction override.”

“Backend engineer with hands-on experience building Flask REST APIs (auth, CRUD, S3 media uploads) and driving measurable Postgres/SQLAlchemy performance gains (p95 reduced to 200–400ms by eliminating N+1s and switching to keyset pagination). Implemented multi-tenant isolation with strict tenant scoping plus Postgres RLS, and built an OpenAI-powered quiz generation pipeline using queued workers, structured JSON outputs, and Celery/Redis optimizations to stabilize high-throughput workloads.”

“Built and deployed a retail customer review intelligence platform by fine-tuning BERT for sentiment/topic extraction and pairing it with a recommendation component. Demonstrates strong production ML rigor (error analysis, relabeling/active sampling, thresholding/guardrails, OOD checks) and AWS-based orchestration at scale (Lambda + SageMaker with batching and concurrency controls), plus proven ability to align non-technical stakeholders on measurable outcomes.”

“Built a production LLM-powered RAG agent for healthcare/insurance operations that retrieves and summarizes patient medical documents with grounded citations, scaling to ~4.5M records. Addressed medical shorthand and terminology by fine-tuning ~120 lightweight DistilBERT models by specialty and validating entities against SNOMED/RxNorm, while using SHAP/LIME and human-in-the-loop review to make decisions explainable to stakeholders.”

“Data/analytics professional with experience across manufacturing and enterprise environments (Wisconsin School of Business project with CNH Industrial; roles/projects at Ascensia Technologies, S&C, and Adobe). Has hands-on work combining warranty/lifecycle tables with technician free-text notes using TF-IDF + tree models (XGBoost/Random Forest), and deep experience in entity resolution/reconciliation across mismatched financial systems using Python/SQL and fuzzy matching, with production-grade pipeline practices in Azure Data Factory/Databricks.”

Praveen Nutulapati

Screened

Mid-level Generative AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

New York, NY6y exp

“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”

“Software engineer with experience at Striim and Amazon who ships end-to-end production systems across UI, backend, ML, and operations. Built a real-time PII detection capability for a streaming data platform by integrating Python ML inference into a Java monolith via gRPC sidecars, achieving ~3M events/hour throughput and ~93% accuracy, and helped drive enterprise adoption (Fiserv, CVS). Also modernized internal Amazon tooling for multi-region scale with modularization and fully automated deployments.”

Shriya Bannikop

Screened

Mid-level Software Engineer specializing in cloud platforms, data engineering, and distributed systems

Seattle, WA5y exp

“Full-stack engineer who built and owned an AI-assisted job-matching dashboard in Next.js App Router/TypeScript, keeping LLM logic server-side and improving performance via deduplication, caching/revalidation, and streaming (35% fewer duplicate LLM calls; 40% faster first render). Also has strong data/backend chops: designed Postgres models and optimized queries at million-record scale (1.8s to 120ms) and built durable AWS multi-region telemetry workflows with idempotency, retries, and monitoring.”

“Walmart engineer who built and shipped a production LLM+RAG system to automate triage and analysis of computer support chats/tickets, producing grounded, schema-constrained JSON outputs for summaries, urgency, and routing recommendations. Emphasizes reliability (hallucination control, confidence thresholds, human-in-the-loop) and runs end-to-end pipelines with Airflow and AWS-native orchestration, plus rigorous evaluation and monitoring tied to business KPIs.”

“AI/ML engineer (Cognizant) who built a production, real-time credit card fraud detection platform combining deep-learning anomaly detection with an LLM-based explanation layer. Strong focus on regulated deployment: addressed class imbalance and feature drift, and added guardrails (SHAP/structured inputs, fine-tuning on analyst reports, rule-based validation) to keep explanations accurate and compliant. Orchestrated the full pipeline with Airflow + Databricks/Spark and used MLflow/Prometheus plus A/B and shadow deployments for measurable reliability.”