Vetted SQL Professionals

“Built and shipped a widely adopted, production-grade RAG internal search assistant that unified scattered engineering knowledge, deployed as a FastAPI service on Kubernetes with FAISS + LangChain. Demonstrates deep practical expertise in retrieval tuning (chunking, hybrid search, re-ranking) and in making LLM workflows reliable in production via guardrails, monitoring, and evaluation, plus strong cross-functional delivery with non-technical operations teams.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Backend/ML engineer who built a job-search automation SaaS using a modular Selenium ETL pipeline, rigorous testing/observability, and a cost-optimized two-pass LLM ranking approach. Has led high-integrity data extraction from messy multi-city PDF records (95% integrity) and managed modular production rollouts for a 20+ engineer team, with a strong security focus (deny-by-default, row-level access control) in an AI-assisted grading platform.”

“Backend/data engineer focused on high-scale, event-driven AWS ingestion systems (SQS/Lambda/EKS) processing millions of events per day, with strong reliability patterns (idempotency, DLQs, bounded retries) and deep observability using Datadog distributed tracing. Has delivered Terraform/GitHub Actions CI/CD and improved secret rotation via Secrets Manager + IRSA, plus Glue-based ETL with schema-evolution handling and Postgres SQL optimization (including JSONB/GIN indexing). Candidate is currently living outside the US and states they do not have US work authorization.”

“Robotics software engineer who built a full LiDAR SLAM pipeline from scratch in C++ (ICP, pose graph optimization, loop closures) and validated it quantitatively against ground-truth datasets. Extensive ROS2 experience from academics and an internship building a localization system, plus practical deployment work using Docker across x64 and ARM edge devices; also trained RL policies for TurtleBots in Gazebo.”

“Software engineer who has shipped both core web platform features (secure user authentication/profile management) and production LLM systems. Built an internal documentation knowledge assistant using a full RAG pipeline (OpenAI embeddings, vector DB, semantic search, reranking) with evaluation loops and a scalable document-ingestion pipeline for PDFs/FAQs, iterating based on metrics and user feedback.”

“Robotics software candidate whose thesis focused on multi-robot warehouse coordination using MAPPO reinforcement learning, trained in simulation (LBF environment, Isaac Sim/RViz) and deployed onto three real-time robots. Built custom ROS 2 Humble nodes for multi-robot control with namespaces, TF broadcasting, and an RL pipeline integrating LiDAR odometry and camera observations.”

“Performance marketing specialist with hands-on ownership of $50K+/month spend on the AdOn Network at RhythmOne, focused on driving new-user growth and on-site engagement. Known for rigorous, variable-isolated testing across keywords, placements, and ad copy, plus clear client reporting tied to engagement and efficiency metrics.”

“Backend engineer with health-tech AI platform experience who designed a modular FastAPI/PostgreSQL architecture supporting real-time user data and swap-in AI workflows. Has hands-on production experience with observability (CloudWatch, structured logging, LangSmith/LangGraph/LangChain tracing), secure auth (OAuth2/JWT, RBAC, RLS), and careful data-pipeline migrations using parallel runs and rollback planning.”

“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”

“Unity gameplay engineer with experience across multiple genres (city builders, match-3, RTS) including implementing an open-world isometric fog-of-war system optimized via chunking and multithreading. Has shipped multiplayer features using Photon/UNet and has dealt with real-time synchronization/ownership conflicts, and also applies AI automation to speed up code reviews and improve Jira/merge request workflows.”

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“CoreAI intern at The Home Depot who improved the Magic Apron Assistant by building a production video ingestion + RAG retrieval system for long videos (uploads and YouTube), including a graph-based retrieval module to speed up and improve relevance. Experienced with Kubernetes orchestration (HPA) and production reliability practices like caching, monitoring, regression testing, and stakeholder-driven requirements.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“Systems Engineer at Tata Consultancy Services with hands-on ownership of enterprise logistics microservices (Spring Boot) using Kafka integrated with Azure Event Hubs, including partitioning strategies and operational handling of consumer lag/duplicate events. Also built a full-stack road-accident blackspot detection application using Python-based spatial clustering and model evaluation with a JavaScript/Mapbox frontend.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Built an AI-based voice interviewer platform at 7C Lingo to automate early-stage candidate screening, owning the full lifecycle from architecture through deployment and weekly production iterations. Implemented a TypeScript/Next.js recruiter dashboard with a Flask/Postgres backend and AWS S3, plus modular services for transcription/analytics/session management using state-driven async workflows. Also created an internal Whisper-powered transcription and editing tool that evolved into a collaborative, versioned, live-transcription system.”

“Backend-focused engineer who owned an end-to-end Python/Flask service at Viasat powering a 1000+ user internal React app, including API design, Postgres performance tuning (~50% faster), Dockerization, and CI/CD. Demonstrated strong problem-solving by building custom EDN parsing logic and has built near real-time AWS SQS/Lambda pipelines with DLQs and autoscaling patterns; currently ramping on Kubernetes/GitOps (ArgoCD).”

“Backend engineer who owned an order management API built with Python/FastAPI and PostgreSQL, integrating payment and shipping providers with strong reliability patterns (idempotency, async workers, retries/backoff, circuit breakers). Experienced deploying services to Kubernetes using a GitOps model with ArgoCD (auto-sync, self-healing, pruning, rollbacks) and building high-volume Kafka streaming pipelines. Has also supported phased cloud-to-on-prem migrations with a focus on security monitoring/SIEM log continuity.”

“Built an AI-powered loan automation dashboard using React and open-source JavaScript libraries, with hands-on experience improving real-world performance by reducing re-renders and optimizing/caching multiple API calls. Also produced developer-friendly API documentation for a voice assistant project, helping teammates integrate features faster with fewer errors.”

“Built ReflectlyAI, an AI-powered interview coach, implementing a low-latency Python/Flask backend with modular LLM/Whisper services, retries/fallbacks, caching/batching, and async/background processing. Demonstrates strong PostgreSQL/SQLAlchemy performance tuning (EXPLAIN ANALYZE, composite indexes, selectinload) and multi-tenant isolation patterns (tenant-scoped schemas, tenant_id middleware), reporting ~50% response-time reduction.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Software engineer who built and open-sourced reusable React/Node.js modules (chat, auth, caching) from an AI education platform, emphasizing intuitive APIs and strong documentation. At TCS, improved a healthcare scheduling platform by diagnosing SQL/server bottlenecks and redesigning database + caching, cutting appointment load times by ~40% and reducing support complaints.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”