Vetted SQL Query Optimization Professionals

“Full-stack/ML-leaning engineer who has shipped production-grade real-time analytics and an internal AI support assistant using RAG over enterprise documentation. Demonstrates strong systems thinking across scalability, reliability, observability, and LLM safety/evaluation (thresholded retrieval, RBAC, response validation, regression-gated evals), with concrete iteration based on performance metrics and user feedback.”

Yash Amre

Screened

Intern Data Scientist specializing in machine learning and NLP

California, USA1y exp

“Analytics-focused early-career candidate with internship experience owning reporting and system performance analysis projects end to end. They combine SQL data preparation, Python automation, and dashboard delivery with measurable impact, including roughly 50% less manual reporting and about 20% better forecast accuracy.”

“Full-stack product engineer focused on data-heavy dashboard applications, with hands-on ownership from React/TypeScript UI through Node/Express APIs to Postgres schema design and optimization. Stands out for combining product sense with engineering rigor: improving onboarding and reporting flows using analytics and user feedback, while also building reusable upload infrastructure and safe multi-tenant configurable experiences.”

Panita Vaishnavi Kanaguduru

Screened

Mid-level Business Analyst specializing in retention, churn, and revenue analytics

Remote4y exp

“Early-career data analyst with hands-on experience at SuperWorld building SQL and Python analytics pipelines for product and growth use cases. They stand out for turning messy event and transaction data into validated funnel datasets, automating reporting to cut manual effort by ~40%, and partnering with product and marketing teams on conversion and engagement metrics.”

“Full-stack product engineer with strong web3/blockchain experience, having built invoicing/tokenization, transaction explorer, and no-code workflow products across React, Node.js/serverless, and SQL. Stands out for combining deep technical execution with product thinking—improving onboarding completion by ~40%, shipping quickly under ambiguity, and creating reusable platform primitives for auditability and multi-tenant customization.”

“Built and deployed a full-stack web product for international football fans visiting the U.S. for FIFA, owning everything from crawling and aggregating event data to frontend, backend, deployment, and maintenance. Particularly strong in data-heavy product work, using LLMs, Google Maps API, and SQL/RPC patterns to improve data quality, speed implementation, and support a polished user experience.”

Abhishek Ghaisas

Screened

Intern-level Data Scientist specializing in AI and full-stack applications

Pune, India1y exp

“Engineer with hands-on experience building production ML and Python backend systems, including a real-time social media monitoring pipeline handling 1000+ events per second and a prototype AI operations assistant for Seattle-Tacoma Airport. Stands out for combining reliability engineering, automation, and LLM/NLP-to-SQL work, with measurable impact such as improving uptime from 92% to 99.4%.”

“AI/automation-focused implementation engineer who has owned customer-facing LLM deployments end-to-end, spanning support automation, lead outreach, and messy document-processing workflows. Stands out for combining hands-on technical depth in Python/OpenAI/RAG systems with measurable business impact, including cutting support resolution time from 24 hours to 6 hours and reducing manual outreach work by 60%.”

“AI-focused software engineer who has built production CRM intelligence features including audio transcription, summarization, and action-item extraction, plus a multi-agent LLM/NLU pipeline using Supabase, Node.js, RabbitMQ, and CloudWatch. Stands out for a disciplined approach to AI-assisted coding: treating AI like a junior developer, rigorously testing outputs, and refining prompts to prevent hallucinations in real business workflows like resume screening.”

Shalini Jada

Screened

Mid-level Full-Stack Engineer specializing in Applied AI

Bridgeport, CT4y exp

“Full-stack AI engineer focused on operational and healthcare analytics use cases, with hands-on experience building React/TypeScript frontends and Node/FastAPI/Flask backends for agentic systems. Stands out for combining LLM orchestration, retrieval grounding, and human-in-the-loop controls with measurable business impact, including a fraud detection dashboard that achieved 92% accuracy and cut manual review time by 85%.”

“Full-stack software engineer with hands-on experience shipping AI-driven product experiences, including a conversational travel planner and a RAG-based PDF question-answering system. Has also built enterprise automation APIs at Accenture for network diagnostics, combining backend engineering, testing automation, and user-focused product simplification for non-technical operations teams.”

“Full-stack engineer with startup SaaS experience building workflow automation and case management platforms for business operations teams. Strongest in Python, TypeScript/React, and PostgreSQL, with hands-on ownership from backend architecture and APIs to production deployment on AWS; notably helped reduce manual processing and improve customer turnaround times in a high-ambiguity scaling environment.”

“Backend engineer focused on real-time, event-driven systems (Java microservices) handling high-frequency data with low-latency and reliability requirements. Strong in Kafka-based asynchronous architectures, Redis caching, JVM/query tuning, and scalable deployments on Docker/Kubernetes with Jenkins CI/CD; no direct ROS/robotics experience but has closely related distributed communication patterns.”

“Backend engineer experienced in evolving high-scale legacy on-prem systems into cloud-native, event-driven microservices on AWS/Kubernetes (noted peak traffic ~1.5M QPS). Strong focus on reliability engineering and operational excellence—SLO-driven observability, GitOps/canary rollouts, chaos testing, and preventing cascading failures (e.g., retry-storm mitigation).”

“Full-stack engineer with recent enterprise experience building Spring Boot/Spring Cloud microservices on AWS (Lambda, S3, DynamoDB) and a React/TypeScript frontend. Has hands-on experience solving microservice communication timeouts via API Gateway/load balancing and implementing centralized JWT-based security, plus performance work for large data workloads using indexing, caching, and async processing.”

“Backend engineer with fintech/transaction-processing experience who built and optimized a Spring Boot + PostgreSQL + AWS service handling money transactions, resolving peak-traffic latency via query/index and connection pool tuning. Shipped an LLM-driven risk-flagging workflow integrated via a FastAPI Python service, owning prompt design, validation guardrails, monitoring, and human-in-the-loop escalation to reduce false positives and improve precision over time.”

Tanisha Dalwadi

Screened

Mid-Level Full-Stack Product Engineer specializing in TypeScript and React

Arizona, USA4y exp

“Software engineer and co-founder with 0-to-1 SaaS experience who built and owned an end-to-end reporting/analytics dashboard on Next.js App Router + TypeScript, including Postgres schema design, aggregation query optimization, and post-launch performance/monitoring. Has delivered measurable React dashboard performance gains (~35% improvement in time-to-insight) and built durable, idempotent job/state-machine workflows using serverless functions and Postgres.”

Anita Bhagashetti

Screened

Mid-Level Software Engineer specializing in distributed systems and cloud microservices

3y exp

“Built and productionized a RAG-based semantic search system for video-derived data, focusing on measurable success metrics (p95 latency, reliability, cost/request) and strong observability (prompt versions, retrieved docs, tool calls, token usage). Experienced in diagnosing real-time issues in LLM/agentic workflows and in supporting go-to-market efforts through tailored technical demos, rapid POCs, and post-close onboarding.”

Pavan Kalyan Reddy Gunamareddy

Screened

Mid-level Full-Stack Java Developer specializing in FinTech and Healthcare

TX, USA4y exp

“Backend/platform engineer in fintech/payments (NexaBank/NextBank/Nexon Bank) who has built Kafka-orchestrated Java/Spring Boot microservices around a PostgreSQL double-entry ledger. Led production-critical reliability work preventing duplicate payment postings via idempotency and offset sequencing fixes, and shipped real-time ML fraud scoring (Python model API + Redis caching) with rigorous evaluation/monitoring (Prometheus) and workflow automation for dispute resolution.”

hetvi patel

Screened

Mid-level Software/Data Engineer specializing in cloud ETL pipelines and data infrastructure

New Jersey5y exp

“Backend/data engineer who built a production analytics data service (Python/FastAPI on AWS/Postgres with PySpark ETL) handling millions of records per day and drove major latency improvements (10–15s to <2s) via indexing, Redis caching, and shifting aggregations into ETL. Also shipped an LLM-based natural-language-to-SQL assistant end-to-end with strong guardrails (schema restrictions, read-only validation, RBAC, masking) and designed a multi-step agent workflow with verification and fallback logic.”

Jayanth Amol Ergola

Screened

Mid-Level Software Engineer specializing in full-stack development and data engineering

Milpitas, California4y exp

“Backend engineer with production experience at KeyBank building high-volume Java/Spring Boot services on Azure with PostgreSQL/Oracle, including async job ingestion and tracking. Demonstrates strong reliability/performance debugging (HikariCP pool exhaustion, DB contention) and has shipped an LLM-powered data analysis/summarization feature with robust production guardrails (validation, shadow testing, deterministic fallbacks, audit logs).”

Tamanna Nandlal Choithani

Screened

Entry-level Full-Stack Engineer specializing in AI and distributed systems

California, USA1y exp

“Full-stack engineer who built an AI-based inventory/procurement query system at Botlily/Botlerly using Flask and Google Sheets as a live knowledge base, overcoming Sheets latency with caching and structured in-memory models. Demonstrated strong LLM product engineering (40% accuracy improvement via preprocessing/prompting) and customer-driven iteration with bar/restaurant owners, evolving the tool into a more comprehensive inventory management and forecasting solution.”

Yanet Fernandez

Screened

Junior Data Analyst specializing in healthcare analytics

Fort Myers, FL2y exp

“Analytics/data professional with hands-on experience turning messy semi-structured CRM JSON data in Snowflake into clean reporting layers using SQL and validation logic. Brings a practical mix of data engineering, Python automation, metric design, and stakeholder alignment to improve reporting accuracy and speed of decision-making.”