Vetted Indexing Professionals

“Backend engineer who built and shipped an end-to-end AI outreach product (LazyMails) combining a LinkedIn-scraping Chrome extension with a FastAPI/Postgres backend and Gemini-powered email generation, achieving major personal productivity gains. Also has enterprise experience at TCS on Humana’s 500k+ user wellness platform running Kubernetes microservices with Azure DevOps CI/CD, plus Kafka-based real-time eligibility event streaming and GitOps-driven operations.”

“Robotics software engineer focused on sim-to-real deployment: built an Isaac Sim/Isaac Lab PPO training pipeline with domain randomization for vision-conditioned quadruped locomotion and integrated a RealSense D435i into a ROS2 stack on hardware. Also worked on an autonomous surface vessel, standardizing ROS2 interfaces across Jetson, microcontroller, GPS/IMU and motor controllers, using structured logging/replay to debug real-time oscillations and improve path tracking.”

“Backend/LLM engineer who ships production internal tooling end-to-end: automated data-request processing with monitoring-driven improvements (better error diagnostics and lower latency via query/index tuning). Also built a RAG-based internal Q&A system over company docs and operational logs with guardrails (similarity thresholds, fallbacks, response limits) and an eval loop using real user queries and human review to drive prompt/retrieval changes.”

“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”

“Data engineer with ~4 years of experience building Python-based data ingestion/processing services and real-time streaming pipelines (Kafka/PubSub + Spark Structured Streaming). Has deployed containerized data applications on Kubernetes with GitLab CI/Jenkins pipelines and applied GitOps to cut deployment time ~40% while reducing config drift. Also supported a legacy on-prem data warehouse/backend migration to GCP using phased migration and parallel validation to meet strict reliability/SLA needs.”