Vetted Batch Processing Professionals

“Backend engineer working on high-volume insurance claims intake systems who shipped a production GenAI document-classification capability in Spring Boot microservices. Emphasizes reliability in LLM systems (strict schemas, confidence thresholds, monitoring, and manual-review fallbacks) and runs evaluation loops with labeled historical documents to drive prompt/validation improvements and reduce manual review.”

“Data engineer focused on AWS-based enterprise data platforms, owning end-to-end pipelines from multi-source batch/stream ingestion (Glue/Kinesis/StreamSets/Airflow) through PySpark transformations into curated datasets for Redshift/Snowflake. Emphasizes production reliability with strong monitoring/observability and data quality gates, and reports ~30% performance improvement plus improved SLAs and latency after optimization.”

“Backend engineer (Marsh McLennan) who evolved a high-volume claims automation pipeline in Python, emphasizing thin APIs with background job processing, strong validation/retries, and production-grade observability. Experienced in secure FastAPI API design (centralized JWT/RBAC), multi-tenant Postgres/Supabase-style row-level security, and low-risk refactors using parallel runs and feature flags; targeting founding-engineer scope roles.”

“Data engineer focused on reliable, production-grade pipelines and data services: has owned end-to-end ingestion-to-serving workflows processing millions of records/day, using Airflow, Python/SQL, and PySpark. Demonstrates strong operational rigor (monitoring, retries, idempotency, backfills) and measurable outcomes (98% stability, ~30% faster processing), plus experience exposing curated warehouse data via versioned REST APIs.”

Tina Nguyen

Screened

Mid-Level Software Engineer specializing in iOS and full-stack development

Sunnyvale, CA3y exp

“Cross-platform (web + mobile) product engineer working on coupon clipping experiences. Built and shipped category-based filtering informed by external market data (Rakuten/Honey) and internal user-journey analytics, validated via A/B testing and resulting in a 30% traffic lift. Experienced handling on-call production incidents, including rapid root-cause analysis and hotfixing a mobile crash that was blocking a release.”

Sharanya Rao

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare

Remote, USA3y exp

“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”

Aishwarya Yenumula

Screened

Mid-level Full-Stack Java Engineer specializing in microservices, React, and Azure

Austin, TX5y exp

“Full-stack engineer with hands-on ownership of a real-time loyalty rewards notification system at Dell, spanning React UI, Spring Boot/Node microservices, Kafka event processing, and Oracle/Postgres persistence. Strong production operations experience across AKS/Azure DevOps and AWS (EC2/RDS/S3, autoscaling, CloudWatch), including resolving peak-load Kafka lag and API latency incidents through scaling and performance tuning.”

“Data Engineer (TCS) who owned an end-to-end CRM analytics pipeline for Bayer’s eSalesWeb integration, ingesting from Salesforce APIs/databases/S3 and serving analytics-ready datasets via PostgreSQL/S3 for Tableau. Drove measurable outcomes: ~60% reduction in manual data-quality effort, ~30% lower latency through SQL optimization, and ~35% improved stability via monitoring, retries, and idempotent processing.”

“Full-stack product engineer who has owned complex internal platforms end-to-end, spanning React/TypeScript frontends, Flask/Redis backend systems, and relational data design. Particularly strong at turning technically dense workflows into intuitive user experiences, including a synthetic-imagery platform adopted by multiple Army research labs and a marketing analytics system with 99.99%+ uptime.”

“Full-stack engineer with hands-on experience spanning React/TypeScript frontends, Cloudflare serverless RAG systems, SQL-heavy backend redesigns, and computer vision workflows. He has shipped practical automation and reliability improvements with measurable impact, including cutting a video-validation reporting process from a week to 2 days and fixing a memory-heavy shipment system before Black Friday to support 30K+ orders successfully.”

“Built an AI-powered document extraction system at Copart that moved from initial POC to rollout across multiple yards, with customer-driven iteration including batch processing. Has hands-on full-stack experience spanning Java/Spring Boot backends, React frontends, and LLM-based workflows, with attention to reliability through multi-LLM fallback, retries, and post-processing.”

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.”

“Frontend engineer with experience delivering complex, data-heavy React + TypeScript dashboards in financial services (Morgan Stanley), including React 18 migration and rigorous quality practices (~80% test coverage). Also improved an existing collaboration product (Heycollab) by reducing duplication and boosting performance ~30% using component modularization, API optimizations, code splitting, and virtualization; experienced with phased rollouts and feature flags for risk-sensitive releases.”

“Engineer with Deloitte experience building real-time analytics products and scalable Kafka/Go/Postgres pipelines, plus production LLM features using RAG and embeddings. Demonstrates strong focus on performance, reliability, and guardrails/evaluation loops to reduce hallucinations and improve real-world AI system quality.”

Nagaveda Sai Kumar Reddy Gajjala

Screened

Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud integrations

Richardson, TX4y exp

“Backend engineer with enterprise SaaS experience (Zoho) who owned an end-to-end cloud integration between Endpoint Central and ServiceDesk Plus, redesigning device onboarding across 64+ scenarios and building a fault-tolerant sync engine that recovered 100% failed transactions. Also built and operated production systems across the stack—FastAPI services with strong testing/observability, React+TypeScript portals, PostgreSQL performance tuning, and AWS deployments with real incident response (RDS CPU saturation resolved with zero downtime).”

Cia Hang

Screened

Executive IT and Operations leader specializing in digital transformation and security

Chicago, IL13y exp

“Candidate is very familiar with the venture capital and broader investment landscape, but is not interested in founding a company. They have worked with several TPG-backed or TPG-owned organizations, helping drive business scaling, cost reduction, and execution against investor governance requirements.”

“Lead full stack Java developer with deep experience building compliance-sensitive government financial systems, including payment distribution, escrow hold management, reallocations, and disbursement workflows. Stands out for combining Angular/React frontend work with Spring Boot, Oracle SQL/PL-SQL, and batch/JMS architecture to stabilize high-volume transaction systems, automate manual workflows, and reduce production support issues.”

“Backend engineer who built a real-time, event-driven alerting platform (Java/Spring Boot, Kafka, MongoDB) processing millions of events per day on AWS (Docker/Kubernetes), including hands-on performance debugging of Kafka consumer lag at peak. Also shipped an end-to-end LLM-based alert summarization feature and designed a multi-step incident triage agent workflow with retries and human-in-the-loop escalation.”

“Accenture engineer who owned an insurance e-application end-to-end and drove incremental releases that reduced recurring production issues. Also built a TypeScript/React (Next.js) + NestJS microservices platform using PostgreSQL, Redis, Stripe, and Kafka, with strong focus on decoupling, eventual consistency, and scaling consumers under load. Created a hackathon chat-based internal assistant that used live form context and documentation-grounded answers to help agents resolve customer queries during form filling.”

“LLM/agentic workflow engineer with healthcare domain experience who built a HIPAA-compliant multi-agent RAG system for clinical review automation at UnitedHealth Group, achieving 92% precision and cutting latency 40% through async orchestration and Redis semantic caching. Also has strong data engineering orchestration background (Airflow on AWS EMR with Great Expectations) and a proven clinician-in-the-loop feedback process that improved model faithfulness by 18%.”

“AI/ML Engineer with 4+ years of experience (Capital One, Odin Technologies) and a master’s in Data Analytics (4.0 GPA) who has deployed LLM/RAG systems to production for compliance/risk and document review. Strong in orchestration and MLOps (Airflow, Kubernetes, MLflow, GitHub Actions) and in tackling real-world LLM constraints like latency, context limits, and data privacy, with measurable impact (20%+ manual review reduction; 33% faster release cycles).”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

Hiya Kothari

Screened

Intern Full-Stack Software Engineer specializing in AI/ML and cloud

San Francisco, CA3y exp

“Built a Python-based geospatial machine learning backend for PFAS contamination risk mapping, including reproducible feature pipelines, ensemble modeling, and a FastAPI layer for visualization/analysis. Emphasizes data integrity and robustness (CRS/coverage checks, fail-fast validation) and has led safe backend refactors using feature flags, idempotent backfills, and Postgres RLS for secure, queryable results delivery.”