Vetted MySQL Professionals

“Built a production AI-powered workflow automation system at Netflix that integrated OpenAI and LangChain with FastAPI services on AWS, cutting roughly 320 hours of manual operational effort. Brings a mix of full-stack product development and practical AI systems experience, with strong attention to reliability, maintainability, and non-technical user adoption.”

“Backend/data engineer with hands-on production AWS experience spanning serverless APIs (Chalice/Lambda/API Gateway/Cognito) and data pipelines (Glue PySpark + Step Functions). Has modernized a legacy SAS reporting system into AWS microservices and implemented schema-drift detection and incident prevention for ETL workflows, plus measurable SQL tuning wins (30 min to <10 min runtime).”

“LLM/RAG engineer who built a production-style multi-agent orchestrator for resume-to-recommendation workflows (PDF ingestion through screening and recommendations), emphasizing prompt tuning and strict JSON output contracts. Currently building a RAG application for an NGO using Airflow (DAGs + embeddings) and tackling messy, missing/imbalanced data; has hands-on retrieval stack experience (FAISS/HNSW, bge embeddings) and uses rigorous evaluation metrics for groundedness and hallucination control.”

“Built and deployed a production LLM-powered risk assistant for KPMG and Freddie Mac that lets analysts query a confidential Neo4j risk graph in natural language (no Cypher), turning multi-day analysis into minutes with traceable, cited answers. Implemented rigorous guardrails, deterministic verification, RBAC/security controls, and a full eval/observability stack, cutting query error rate by ~50% and iterating through weekly UAT with non-technical risk analysts.”

“Gameplay engineer with hands-on ownership of a real-time C++ combat ability system, including diagnosing and eliminating large-scale combat frame spikes by refactoring hit detection to an event-driven, animation-notify approach (cut collision checks ~80%). Also implemented UE5 networked abilities (dash) with client-side prediction and server-authoritative reconciliation, plus projectile ballistics validated through debug spline visualizations and unit tests.”

“Series C (Sigma Computing) full-stack engineer/intern who shipped production features across React/TypeScript and GraphQL, including a Recents/workbook activity reliability improvement that handled unsaved “exploration” events via deterministic backend updates. Emphasizes production quality through Jest/Cypress coverage and feature-flagged staged rollouts, and is recognized for UX-focused improvements (fast, accurate filtering at scale) and proactive cross-functional ownership.”

“Data engineer at Microsoft who owned an end-to-end subscription analytics platform processing 7TB+ daily across 40+ pipelines, combining ADF batch ingestion with Kafka/Spark streaming and rigorous Great Expectations quality gates. Built a Fabric-based self-service ingestion platform with CI/CD and observability, plus a Databricks feature store serving near-real-time ML inference with Delta Lake reliability and versioning.”

Dheeraj Kumar

Screened

Intern Data Scientist specializing in marketing analytics and data engineering

Tucson, Arizona2y exp

“AI/LLM practitioner with internships at Dell Technologies and Roche who built and deployed a healthcare-focused "Doctor LLM" by fine-tuning Meta Llama 3.2 on healthcaremagic.json, emphasizing safety guardrails to prevent harmful medical advice. Experienced in productionizing AI workflows with monitoring, testing, and orchestration (Airflow, Kubernetes), and in delivering AI-agent-driven competitive landscape insights to non-technical business stakeholders.”

Zheng Wu

Screened

Junior Software Engineer specializing in backend systems and cloud messaging

Mountain View, CA1y exp

“Data/ML engineer who has owned end-to-end systems across email deliverability/segmentation and production LLM apps. Built a Spark+Airflow segmentation engine that materially improved deliverability (99.9%) and open rates (>50%), and shipped a PDF-to-quiz RAG product using LangChain/Vertex AI/Chroma with strong guardrails and an eval loop that cut hallucinations to <5%.”

Asrith Velireddy

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems

Harrison, NJ4y exp

“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”

Sri Charan Reddy Mallu

Screened

Mid-Level Software Development Engineer specializing in GenAI and full-stack cloud systems

Redwood City, CA5y exp

“Full-stack engineer with experience across Magna, C3.ai, and Amazon, building GenAI-enabled products and finance transaction systems. Has shipped Next.js (App Router) + TypeScript features backed by Go/Python RAG pipelines, and emphasizes production quality via load testing, Selenium regression coverage, LLM-aware integration testing, and Azure observability. Also built LangGraph-orchestrated multi-step content generation workflows with robust retry/idempotency strategies.”

KARTHIKBABU VADLOORI

Screened

Mid-level Full-Stack Developer specializing in Spring Boot, React, and cloud microservices

San Francisco, CA5y exp

“Backend engineer with experience at Meta and Accenture building regulated-data systems (healthcare/financial) using Python/Flask and Postgres. Has scaled high-throughput services to millions of daily requests, delivering measurable latency wins (~40% API latency reduction; ~35% faster DB-backed endpoints), and has productionized ML inference services using Docker/Kubernetes and AWS (ECS/SageMaker).”

Ching Pei

Screened

Executive product leader specializing in B2B SaaS growth and portfolio integration

Remote11y exp

“Current VP of Product / Chief Product Officer who spent the last two years helping shape fundraising and exit strategy efforts at their company. They bring rare product-led storytelling experience across debt financing, pre-IPO roadshow preparation, and acquisition discussions with private equity and investors, paired with a clear interest in building something from scratch.”

“Built GenMedX, a multi-module clinical AI system for emergency department decision support spanning triage prediction, diagnosis, medication Q&A, and visit summarization. Stands out for combining medical LLM fine-tuning, RAG, and rigorous evaluation/monitoring to drive a major triage recall improvement from 38.5% to 76.6%, with a strong focus on safety, edge-case detection, and production reliability.”

Meredith Ma

Screened

Entry-level AI/ML Software Engineer specializing in generative AI and computer vision

Pittsburgh, PA2y exp

“Built and owned a production RAG coding assistant at Magna International used by 200 engineers, with hands-on work across React/TypeScript, retrieval infrastructure, and Postgres observability. Also brings an unusual blend of product UX thinking from AR game onboarding work, showing strength in both technical systems reliability and user activation.”

“Technical lead who shipped a production GPT-4-powered customer support agent for Square, serving a large fintech customer base through a React chat interface with tool-using orchestration, guardrails, and live handoff paths. Brings strong real-world experience in agent reliability, evaluation, observability, and workflow orchestration using Temporal, Sidekiq, Pinecone, Datadog, and Snowflake.”

“Operations leader with Amazon experience owning a founder-level initiative during national supply chain Regionalization, building and scaling a "Stack-to-Light" operating mechanism to standardize non-sortable FC execution with instrumentation and balanced-scorecard metrics. Later joined Revivn to stabilize an underperforming operation by developing frontline leaders and aligning ops with finance and GTM through clear KPIs and operating cadence.”

“AWS engineer who productionized an internal ML-driven data pipeline from a notebook prototype into a scalable, observable Python service (schema validation, deduplication, idempotency, safe retries, versioned transforms, CloudWatch alarms), reducing manual effort and improving data accuracy/trust. Experienced diagnosing workflow issues in real time (e.g., upstream schema changes) and partnering with account managers/support to unblock adoption of seller-facing Marketplace features by demonstrating reliability with concrete metrics.”

“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”

“Built a production RAG-based natural-language-to-SQL system at Global Atlantic to replace slow, expensive manual analytics ticket workflows, focusing heavily on retrieval quality and measurable evaluation (200-question ground-truth set; recall@5 improved 0.65→0.78 via semantic chunking). Also built a custom MCP-style agent orchestrator for a personal project (arxiv-ai) to improve flexibility and Langfuse-aligned observability, and has hands-on experience with LangGraph, CrewAI, and n8n.”

“Built a production LLM-driven archival assistant that turns large, low-quality scanned handwritten files (120+ pages) into structured datasets, overcoming context-window and hierarchy challenges with a two-phase LLM + rules pipeline and reaching 98.1% accuracy (Gemini-2.5 Flash). Also orchestrated a large human-in-the-loop effort with 78 archivists, producing 2,400 high-quality annotations in 4 days via detailed rubrics and support.”

“Senior engineering leader in the Bay Area with experience spanning VMware, Hortonworks/Cloudera, Barracuda, and Palo Alto Networks, including leading open-source work (Apache Knox) and architecting large-scale security platforms. Has driven disaster recovery and cloud security products, designed Python microservices for Microsoft 365 security, and scaled teams (3x) while formalizing enterprise readiness practices with automated documentation using Notebook LLM.”

Chris Du

Screened

Intern Full-Stack Software Engineer specializing in web apps and AI systems

Mountain View, CA0y exp

“Product/UX designer who builds end-to-end systems across both consumer wellness and industrial/technical domains. Designed BloomPath (mental-wellness platform for therapists and young professionals) using research-driven, emotionally safe interaction patterns, and also simplified a Bosch autonomous parking vision-language mapping pipeline into a developer-facing real-time UI with layered debug tooling. Comfortable collaborating deeply with engineers and contributing in React/JS.”