Vetted Software Engineers

“Backend engineer with healthcare-domain experience building a security-critical RBAC identity/authentication/authorization microservice suite used across hospital imaging platforms (X-Ray, Ultrasound, etc.). Demonstrates strong security mindset (mTLS, cert hygiene, JWT, pen-testing collaboration) and pragmatic scaling/reliability practices (Nginx load balancing, Redis caching, automated tests, canary rollouts).”

“Backend engineer with experience in both regulated healthcare and finance: built a multi-agent RAG system to generate FDA regulatory approval documents for biomedical devices, improving retrieval accuracy via hybrid search (semantic + BM25) and hierarchical chunking. Previously at JPMorgan Chase, led a Java microservice refactor and AWS migration using Elasticsearch-first patterns, caching, and safe rollout strategies (parallel runs, canary, blue-green) in asset/wealth management.”

“ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.”

“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”

“Robotics software engineer focused on production-grade autonomy in GPS-denied environments, building full navigation stacks (perception, EKF/UKF sensor fusion, planning, control) in ROS2. Integrated YOLOv8/semantic segmentation/RL policies into real-time NAV2 pipelines via a custom perception-aware costmap layer, with emphasis on deterministic control loops, embedded GPU performance, and robust system observability/fault tolerance.”

“Backend engineer who built a Python-based data profiling/statistics platform processing up to 50M rows and ~300 metrics, using a DAG execution model, multithreading, and smart caching to cut processing time by up to 70%. Also improved PostgreSQL query performance from 12s to 2s via indexing/query rewrites, integrated an LLM (LangChain + OpenAI) for explainable “chat with the pipeline” functionality, and designed an AWS EC2+SQS architecture for scalable, isolated per-user processing.”

“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”

“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”

“Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.”

“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.”

“Full-stack engineer with production experience at Atlassian and Zoho, spanning GraphQL federation, React/TypeScript frontends, and cloud-native AWS/Kubernetes operations. Built and operated a federated GraphQL gateway with Terraform + CI/CD + observability, delivering major latency and integration-time improvements, and also designed high-volume Kafka data pipelines (10M+ events/day) with strong reliability guarantees.”

“Summer intern on AWS Lambda tooling team who shipped Finch support in AWS SAM CLI, adding OS/runtime detection and robust fallback behavior to preserve Docker compatibility across developer environments. Also built an end-to-end RAG system for querying arXiv quantitative finance papers using Postgres/pgvector with two-stage retrieval, citation-grounded prompting, and rigorous evaluation loops driven by IR metrics and user feedback.”

“Owned and built Bloomberg’s end-to-end bitemporal dividend & dividend-forecast data platform powering BQL for 400k+ terminal users. Architected real-time Kafka ingestion (5k–10k msgs/sec) across 100k+ tickers with strong correctness guarantees (PIT/bitemporal time-travel, immutable history to avoid look-ahead bias) and achieved sub-100ms p95 query latency through indexing and caching, deployed with Kubernetes + DLQ and robust monitoring.”

“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.”

“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.”

“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.”

“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.”

“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.”

“Software engineer with primary experience in Java and Python who also troubleshoots and optimizes JavaScript/React performance issues. Has handled customer-reported production problems via log-driven diagnosis and backend workflow fixes, and took ownership of simplifying and automating a service region-expansion process through time analysis and process documentation.”

“Built an "AskMyVideo" system that turns YouTube videos into queryable knowledge graphs by transcribing audio (Whisper), chunking and embedding content, and enabling traceable answers back to exact timestamps. Strong in entity resolution (rules + fuzzy matching + TF-IDF/cosine with PR-curve thresholding) and modern retrieval stacks (FAISS, hybrid dense/sparse, domain fine-tuning with ~12% precision gain), with a production mindset using Airflow/Prefect, Docker/FastAPI, and LangSmith/Prometheus/Grafana observability.”