Vetted Unit Testing Professionals

“Infrastructure-leaning engineer who has built real-time ML systems end-to-end: a Jetson-deployed adaptive Whisper ASR service (Flask + WebSockets, React/TS UI) and a high-throughput Postgres schema for live transcription. Also delivered customer-facing AI billing/OCR improvements for a dental startup (Dentite), boosting OCR performance by 38%, and has experience instrumenting open-source ML deployment stacks to add infrastructure visibility.”

“LLM/RAG engineer who has shipped production systems in high-stakes domains (fraud analytics at Mastercard and security compliance as a CI/CD gate). Strong focus on reliability: hybrid retrieval for latency, citation-backed outputs for trust, and code-driven eval/regression pipelines using golden datasets. Also built scalable OCR-based ingestion for messy classroom artifacts (handwriting, PDFs, whiteboard photos) using Go/Python and cloud services.”

“Software engineer with experience at Molina Healthcare and Target, owning production features end-to-end across backend, data pipelines, and UI. Built an event-driven claims validation system (Python/Java/Spring Boot/Kafka) with strong observability, and shipped embeddings-based semantic product search with evaluation loops (CTR/top-k + human review) and guardrails like keyword-search fallback.”

Ajay Madhusudhan Thumala

Screened

Junior Software Engineer specializing in data engineering and LLM applications

Irvine, CA1y exp

“Computer science engineer and master’s graduate who independently built a mechatronics-heavy capstone prototype: a smartphone concept for deafblind users using micro-actuator arrays for braille reading. Also has platform engineering experience at Quantiphi, deploying webhooks to Kubernetes and implementing GitOps-based CI/CD using AWS CodeCommit/CodeBuild and ECR.”

Daniel Panrong Dai

Screened

Junior Software Engineer specializing in AI and full-stack development

New York, NY1y exp

“Consulting-background AI practitioner who led a production LLM pipeline on Snowflake Cortex to map hundreds of thousands of messy OCR/form-based contract fields into standardized Salesforce fields, including confidence scoring and an LLM-driven feedback loop. Strong focus on real-world constraints—token limits, cost control, and evaluation without ground truth—paired with frequent stakeholder-facing progress reporting.”

Poojitha MaheshShyla

Screened

Mid-level Java Backend Developer specializing in cloud-native microservices

Sunnyvale, CA5y exp

“Backend-leaning full-stack engineer with Walmart experience building and operating high-volume media upload and processing systems. Strong in Java/Spring Boot, Postgres performance tuning (EXPLAIN/ANALYZE), and durable workflows using Kafka/Spring Batch with retries and idempotency, plus production ownership via monitoring and optimization; familiar with Next.js/TypeScript and modern React performance patterns.”

Bhanu Prakash Reddy Dakilli

Screened

Mid-level Data Engineer specializing in Azure ETL/ELT and data warehousing

Framingham, MA4y exp

“Data engineer who has owned end-to-end production pipelines for customer transaction data (~2–5 GB/day) using Python/PySpark/SQL and Airflow, delivering major reliability and speed gains (70% faster reporting; 60–70% fewer data issues). Also built a daily external web-scraping system with anti-bot handling and safe, idempotent Airflow-driven backfills, plus a Python data API optimized with indexing/caching and tested for correctness.”

Aditya Jhaveri

Screened

Mid-level Software Engineer specializing in AI, big data, and distributed systems

Jersey City, NJ3y exp

“Software Developer at NYU (GEMSS) focused on scaling and optimizing a data-heavy asset management web app, including migrating/optimizing data access via Google Sheets API and Firestore. Previously an SDE at Sainapse working on Spring Boot microservices POCs (Kafka, Hadoop at 2B+ record scale). Built an end-to-end Apple Wallet coupon generation/redemption system using PassKit + Google Apps Script with measurable ops impact (40% efficiency gain).”

Yash Priyadarshi

Screened

Junior Software Engineer specializing in cloud infrastructure and distributed systems

Bengaluru, India2y exp

“Backend/distributed-systems engineer who built a Golang distributed key-value store on AWS using Multi-Paxos, WAL, and non-blocking gRPC replication (cutting write latency ~40%) and proactively addressed tricky failure modes like leader-election livelock. Also developed a Python/Kubernetes cost-optimization scaling engine deployed with Helm/Terraform, delivering ~$40K annual savings while sustaining 99.99% uptime, and drives contract-first API development (OpenAPI/Swagger) to speed frontend integration.”

Sri Harshitha Yannam

Screened

Junior Software Engineer specializing in AI/ML and cloud platforms

Austin, TX2y exp

“LLM/agent engineer who shipped a production "Memory Assistant" at HydroX AI, building a LangChain/LlamaIndex RAG memory pipeline on ChromaDB/FAISS with robust fallbacks (BERT/BART), prompt-injection mitigation, and 99.9% uptime monitoring. Also built a multi-step customer support agent using Rasa + OpenAI Assistants API with structured tool calling, guardrails, and human-in-the-loop escalation, and has experience hardening agents against messy ERP data via Pydantic validation, idempotency, and transactional outbox patterns.”

“Full-stack engineer with experience shipping production features in both networking (Cisco: ACL object groups, 3G/4G) and fintech/online banking (PLAID, Auto Pay, OLB, membership qualification). Works primarily with React/Java/Hibernate and REST/JSON, emphasizing caching/memoization for UI performance and strong testing/XP practices to maintain quality under faster release cycles.”

“Full-stack engineer with recent JPMorgan experience building GPT-4-powered customer sentiment/feedback analytics products (Next.js 14 App Router + FastAPI + Postgres) and owning them post-launch with CloudWatch/Datadog observability. Also implemented Temporal-based transaction reconciliation workflows with strong reliability patterns (idempotency, retries, DLQ, versioning) and has prior high-scale healthcare dashboard experience at Optum.”

“Built an end-to-end AI resume tailoring system in Python/FastAPI with strong emphasis on reliability, validation, observability, and regression-based quality control. Also demonstrates thoughtful browser and workflow automation skills, especially around brittle UIs, resumable state, and turning ambiguous manual processes into production-ready automated pipelines.”

Saisureshreddy Challa

Screened

Mid-level Data Scientist specializing in AI/ML, LLMs, and domain analytics

California, USA6y exp

“BlackRock AI/ML engineer who built and owned a production LLM document intelligence system for regulatory and investment analysis end-to-end. They combined RAG, multi-agent validation, strong evaluation/monitoring, and reusable Python services to process 50K+ documents, cut review time 40-50%, and improve decision accuracy by about 25%.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”

“ML/AI engineer with hands-on ownership of production computer vision, speech, and legal RAG systems. Notably improved a key-duplication CV pipeline enough to unblock commercial launch and remove specialist manual measurement, and also shipped a live Quran recitation detection feature for a product with 1M+ users.”

“Built a production AI-powered insights platform for marketing teams analyzing large-scale social and news data, combining Java microservices, Kafka, Spark, React, and LLM-based retrieval workflows. Stands out for shipping customer-facing AI features with measurable gains in accuracy and latency, plus solid reliability practices for high-volume backend systems.”

“5-year .NET full stack developer who has applied AI-assisted development in an enterprise Cisco environment, using tools like GitHub Copilot and ChatGPT to accelerate microservice API delivery while maintaining architecture, security, and code quality standards. Notably reports a roughly 30% reduction in development time on a customer policy management/claims processing project through disciplined use of AI for boilerplate, testing, and design validation.”

“Backend engineer with end-to-end ownership experience on a real-time AI-driven payment authorization/orchestration platform at PayPal. They describe strong fintech systems depth across Java/Spring/Kafka microservices, database and latency optimization, and reliability engineering, with concrete impact including 35% fewer processing failures, latency reduced from 420ms to 140ms, 1,200+ weekly manual reviews eliminated, and 40% faster incident response.”

“Founding engineer who has built web products end-to-end in startup settings, spanning FastAPI/React application development, auth, cloud deployment, and Kubernetes-based scaling. Particularly notable for designing custom GPU autoscaling for an AI-style recommendation product and later shipping workflow-driven healthcare support tooling using Temporal, Postgres, and modular backend logic.”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“Robotics software engineer with hands-on GPU/CUDA vision work (solo-built a 4-fisheye panorama stitcher using camera intrinsics/extrinsics) and mapping/localization expertise, including radar-driven pose-graph mapping optimized with Ceres. Strong ROS background (Cartographer, AMCL, TEB) and demonstrated localization improvements by biasing AMCL with Cartographer to reduce drift; experience shipping modules deployed across large robot/vehicle fleets (e.g., retail scanning robots and automotive).”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“AI/ML engineer currently at BlackRock who deployed an AI-powered sentiment analysis microservice into a task management platform to prioritize and escalate high-risk/frustrated tickets from free-text comments. Experienced running production microservices on AWS EKS with Docker/Kubernetes/Helm and provisioning infrastructure via Terraform, with strong MLOps rigor (MLflow evaluation pipelines, canary rollouts, and real-time monitoring) and cross-functional collaboration with product/operations.”