Vetted Python Professionals

“Backend-leaning full-stack engineer with experience at Liberty Mutual and Airbnb, building high-scale insurance claims systems (1M+ monthly transactions) and consumer booking/pricing services (120K–180K daily requests). Strong in transactional data integrity, PostgreSQL performance tuning, and production operations (Docker/Jenkins/AWS), with measurable UX/performance wins including ~2.3s page loads and significant runtime failure reduction.”

“Senior engineering executive who has spent the last decade leading large-scale platform modernization while staying deeply hands-on in architecture, debugging, and code reviews. He has led 120+ person organizations, built cloud-native SaaS and identity platforms across regulated and enterprise domains, and ties engineering decisions directly to revenue outcomes, including contributing to Wind River's $1B+ milestone.”

Sunil Chinthaparthi

Screened

Senior Software Engineer specializing in cloud-native backend and distributed systems

New York, NY4y exp

“Backend engineer focused on Python/FastAPI microservices running on Kubernetes (AWS EKS) with strong GitOps/CI/CD ownership (GitHub Actions + ArgoCD). Demonstrated measurable performance wins (p95 latency cut from >1s to <200ms) and production reliability work across Kafka/Redis streaming and cloud-to-on-prem migrations (RDS/S3 to Postgres/MinIO) using parallel validation and checksum-based consistency checks.”

“LLM/agentic workflow practitioner (Continuum AI) who productionized an LLM system for manufacturing RMA intake and warranty claims by moving from a brittle prompt to a modular pipeline with RAG, function-calling extraction, deterministic validation, and strong observability. Also diagnosed and fixed an agentic ticket-triage misrouting issue by tracing failures to retrieval timeouts, adding guardrails/fallbacks, and implementing retries plus continuous evaluation—bringing misroutes near zero while creating a repeatable debugging playbook.”

“Robotics-focused engineer who built a non-holonomic self-driving car on Raspberry Pi 5 using ROS 2, implementing sensor fusion (robot_localization EKF), 2D SLAM (slam_toolbox), custom Hybrid A*/RRT* planners, and MPC trajectory tracking. Demonstrated strong real-time debugging and performance tuning (timestamp sync, CPU contention mitigation) and is extending the platform toward CV-based plant identification and autonomous plant watering.”

“Gameplay engineer with hands-on experience across Unreal Engine 5 and Unity, owning systems from level streaming/teleportation to motion-matched character movement. Has shipped/implemented multiplayer features including GAS-based replication with prediction/reconciliation and a deterministic lockstep runner using fixed-point math. Strong at diagnosing hard-to-repro physics issues and balancing performance/feel (e.g., mobile voxel A* pathfinding at 60 FPS, Chaos destruction tuned to avoid trapping players).”

“Co-founded and led the business side of Plasma Nutrition for a decade, commercializing atmospheric-plasma technology to improve protein efficacy/absorption. Bootstrapped for 5 years before raising venture funding, leveraged FoodX accelerator validation, and navigated a major go-to-market pivot (B2C to B2B). Drove growth by renegotiating out of an exclusive manufacturing arrangement and scaling to roughly six brand partners within a year.”

“Robotics software engineer at Tesla who led end-to-end development of a distributed real-time control and orchestration platform for autonomous systems. Deep production ROS 2 experience (nav2, slam_toolbox), with demonstrated wins reducing end-to-end latency 25–30%+ via profiling, multithreaded executors, and QoS tuning, plus simulation and deployment at scale using Gazebo/Webots, Docker/Kubernetes, and CI/CD.”

“Internship experience building a production LLM-powered podcast operations agent that automated lead intake (HubSpot), guest research, scheduling (Calendly), meeting-summary evaluation (Gemini), and human approval via Slack bot—while retaining rejected candidates for future outreach. Also contributed to ideation of a multi-agent orchestration framework with parsing and task routing, and emphasized reliability via structured prompts, HITL feedback, and prompt-based test sets.”

“Robotics software engineer who built and owned an autonomous laparoscope tracking system on a UR3e with an eye-in-hand RealSense camera, integrating YOLO-based tool detection with velocity control under a strict RCM constraint and deploying successfully in a hospital setting. Deep ROS2/MoveIt2 experience (architecture, QoS, custom nodes) plus autonomy stack work across SLAM, planning, and real-time latency/control debugging.”

“LLM engineer who has deployed privacy-preserving, real-time workplace risk monitoring over massive enterprise chat/email streams, tackling latency, hallucinations, and extreme class imbalance with model benchmarking, RAG + fine-tuning, and a pre-filter alerting layer. Also built an agentic legal contract drafting system (Jurisagent) using LangGraph/LangChain with deterministic multi-agent control flow, structured outputs, and reliability-focused evaluation/telemetry.”

“Built a production LLM-powered analyst assistant at Discern Security to speed up SOC investigations using a RAG pipeline over security vendor documentation (Python PDF ingestion, vector search). Demonstrates deep, security-critical LLM engineering: structure-aware chunking with custom table parsing, grounded/cited responses, prompt-injection defenses, and post-generation validation, validated via golden datasets and adversarial testing; tool is used daily by analysts.”

“Software engineer who owned customer-facing, high-traffic TypeScript/React + TypeScript backend systems end-to-end, emphasizing safe velocity through feature flags, staged rollouts, observability, and rollback-ready incremental delivery. Reports shipping more frequently with fewer production incidents and faster recovery due to these guardrails.”

“Built and deployed a production healthcare medical review agent that automates call-transcript summarization and medication reconciliation using a hybrid deterministic + LangGraph-orchestrated LLM workflow. Demonstrates strong reliability engineering (guardrails, schema validation, confidence thresholds, golden/adversarial eval, Langfuse monitoring) in a regulated environment, delivering 60% lower latency and 70%+ efficiency gains while partnering closely with care managers and operations.”

“Software engineer with experience at PayPal and JPMC building large-scale onboarding/account setup systems using React/TypeScript with Spring Boot/Node microservices and Kafka. Also built an Ignition-based SCADA monitoring tool at Mainspring Energy that became the default for manufacturing/test engineers by aggregating real-time telemetry and historical test data.”

“Backend-focused software engineer (4+ years) across e-commerce, banking, and healthcare who owned mission-critical checkout/order management end-to-end and improved peak-traffic success rates via resiliency patterns (timeouts/retries/caching) and data-driven iteration. Also built and shipped real-time operational dashboards (React/TypeScript + Spring Boot) using WebSockets and event-stream integrations, with strong experience in Kafka/RabbitMQ-style messaging at scale.”

“Full-stack engineer (~4 years) who has owned and shipped customer-facing SaaS onboarding and a role-based real-time analytics dashboard using TypeScript/React with a modular backend. Experienced in microservices with RabbitMQ and strong observability practices (correlation IDs, structured logging, queue metrics), and built an internal deployment tracker integrated with CI/CD that replaced manual spreadsheet/Slack processes.”

“Built and shipped a production LLM-powered investor research feature for a fintech product, focused on grounded answers and minimizing hallucinations. Implemented retrieval-quality and evidence-coverage gating with clear refusal fallbacks, and evaluates systems with regression tests and metrics like correct-refusal rate, hallucination rate, and latency. Comfortable orchestrating workflows with LangChain or custom Python depending on production needs.”

“Backend engineer focused on cloud/distributed systems, deploying Java 17/Spring Boot microservices on AWS EKS with RDS and Kafka. Demonstrated strong production readiness work (DB lock mitigation, Kafka idempotency, gradual rollouts) and delivered a major latency improvement (~400ms to ~100ms). Also has proven cross-layer troubleshooting skills, isolating intermittent API timeouts to a specific Kubernetes node’s network interface issue, and partners closely with ops teams to build dashboards and workflow automation (including Python scripts).”

“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 a hackathon "Patient Summary Assistant" backend focused on healthcare workflows, combining RAG-based summarization with HIPAA-minded privacy controls (NER redaction + encryption). Demonstrated strong infra skills by deploying on Kubernetes with Helm/HPA and GitOps (ArgoCD), plus migrating from OpenAI to an on-prem Llama 3 stack (vLLM, quantization, shadow-mode testing) and adding real-time Kafka ingestion for patient vitals/anomaly alerts.”

“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.”