Vetted SQL Professionals

Suloni Praveen

Screened

Entry-Level Software Engineer specializing in data engineering and ML systems

Los Angeles, CA0y exp

“Built an end-to-end Next.js/TypeScript LLM-based scientific PDF analyzer using local Ollama/Llama inference to prioritize privacy and cost, producing structured research artifacts (e.g., authors/methods/findings) with ~92% extraction accuracy. At Qualtrics, helped replace a batch pipeline with a real-time, low-latency ML inference service (Python/Go on Kubernetes) using Redis caching, Grafana-based observability, and graceful fallbacks to protect UX during failures.”

Lance Chou

Screened

Intern Machine Learning Engineer specializing in NLP and MLOps

Canada1y exp

“PhD-led research engineer who has shipped LLM-powered agents for automated knowledge extraction from STEM textbooks/papers into a graph database, reporting a 90% accuracy improvement and major reductions in manual curation time. Also built an end-to-end multi-agent news aggregation/sentiment pipeline using the Agno framework with Pydantic-structured outputs, retries, and monitoring, and has experience processing messy SEC filings.”

“Analytics candidate with hands-on experience in both fraud and churn use cases, including SQL-based preparation of 6.5M transaction records and reproducible Python modeling workflows. Stands out for combining technical rigor in data quality, feature engineering, and imbalance handling with strong stakeholder alignment, metric definition, and dashboard adoption.”

“Analytics-focused candidate with hands-on experience using SQL and Python to clean messy business data, automate reporting, and build practical customer analytics solutions. Notable examples include a 70% reduction in reporting time through Python-based Excel automation at Shell and stakeholder-friendly retention/RFM segmentation work for small business clients in freight and winery contexts.”

Atharva Bhide

Screened

Entry Software Engineer specializing in AI/ML and multimodal systems

Los Angeles, CA1y exp

“Built and shipped a production healthcare AI platform for a clinic in Brea, LA that combined LLM-based clinical report generation, voice agents for appointment workflows, and camera-based patient monitoring. Stands out for pairing multimodal AI architecture with production-grade reliability and compliance practices, while delivering concrete business results including 90% workflow automation, 200 hours saved per month, and a 60% improvement in customer retention.”

“Analytics candidate with hands-on experience at BCBS building HIPAA-compliant SQL/Snowflake/Tableau pipelines across fragmented legacy healthcare systems. Stands out for turning a 5-day claims reporting process into a near real-time 10-minute dashboard and for pairing strong data engineering discipline with reproducible Python-based churn modeling that drove measurable retention outcomes.”

“Analytics-focused candidate with strong SQL, Excel, Python, and Tableau skills who supports payroll-, compensation-, and finance-adjacent processes through rigorous data validation and reconciliation. Stands out for uncovering a duplicate-record mapping issue that exposed roughly $250K in revenue leakage and for building repeatable controls, dashboards, and automated checks to improve reporting accuracy.”

“Early-career builder with hands-on project experience spanning Python data processing, a Chrome extension for autofilling job applications, and a sign-language glove system integrating sensors, microcontrollers, and a web interface. Stands out for approaching student and project work with a production-minded focus on validation, modularity, edge cases, and reliability.”

“Built Laborate.app, a full-stack lab notebook and inventory product for scientists, largely solo using Next.js App Router, TypeScript, Postgres, Prisma, and AWS S3. Stands out for combining product ownership with practical concerns like encrypted data storage, autosave reliability, caching, tenant isolation, and scalability planning.”

“Candidate has a business plan and has been actively fundraising for about one month by reaching out to targeted VCs. They are early in learning the VC/accelerator landscape, pragmatic about entrepreneurship, and open to either founding their company if funded or joining an existing company if a strong opportunity arises.”

“Product-minded full-stack engineer with deep experience building React/TypeScript + serverless/SQL systems and improving both operational reliability and user activation. They stand out for connecting low-level architecture decisions like idempotency and atomic updates with business outcomes such as reduced support burden and lower 30-day cancellations, while also designing configurable multi-tenant product systems with strong guardrails.”

“Early-career analytics candidate with hands-on experience in SQL/Python data pipelines, Tableau reporting, and marketing engagement analytics across internship and startup settings. Stands out for combining rigorous data quality practices with practical AI system design, including an end-to-end GPT-4 grading capstone that emphasized explainability and human oversight.”

“Built and owned Alight's AI-powered Search Summary feature end-to-end, using a RAG pipeline with OpenSearch and Bedrock, and drove a 20% increase in user feedback scores. Stands out for bringing rigorous production evaluation to LLM systems via live LLM-as-a-judge monitoring, and for experience with advanced agentic architectures, hybrid search, and reranking at scale.”

Darshi Jain

Screened

Mid-level Customer Success Manager specializing in ads, product strategy, and analytics

San Francisco, CA4y exp

“Revenue-focused seller with experience at Amazon and Vedanta, combining data-driven prospecting, account expansion, and GTM process building. Stands out for turning performance insights into repeatable growth motions—driving $3.1M annual revenue uplift and 40% format adoption at Amazon, while also building new-market playbooks from scratch in high-ambiguity industrial segments.”

“Data-engineering-oriented candidate with hands-on experience building an agentic AI product and operational automation workflows. They described automating inventory-to-ERP discrepancy reconciliation with anomaly detection and daily reporting, and also have practical scraping/automation experience dealing with Cloudflare-protected sites using Selenium and Puppeteer.”

“ML/AI practitioner with hands-on experience shipping production time-series forecasting and RAG-based customer insights platforms in an enterprise setting. At BASF, he improved seed sales forecasting beyond naive baselines using model selection tailored by brand size, and he also led a RAG solution over Salesforce reports, complaints, and surveys that reached 2,000+ users with strong daily engagement.”

Fumin Sun

Screened

Junior Product Analyst specializing in AI-driven FinTech analytics

New York, NY2y exp

“Analytics-focused candidate from Street Diligence who has built operational reporting and product/engagement metrics from messy platform, Jira, and manual-log data. Stands out for turning unreliable manual processes into trusted, repeatable SQL/Python workflows that influenced staffing, weekly targets, renewal prep, and a product matching-logic fix.”

“Candidate is pursuing a CTO role and has helped multiple startups turn early technology concepts into concrete, real-world technical requirements. They cite a systems science and mathematics background, along with experience at JPMorgan Chase, and appear strongest in technical strategy, concept fleshing, and identifying strong people to help teams succeed.”

Amit Dharam

Screened

Junior AI/ML Software Engineer specializing in backend systems and cloud deployment

Tempe, AZ3y exp

“Built multiple end-to-end automation and data systems, including an Accio RAG pipeline combining PDF parsing, FastAPI, Neo4j, and vector search, plus Selenium-based scraping for a virtual try-on product. Stands out for reliability-minded engineering: automated testing, structured logging, validation layers, and a data-driven approach to debugging flaky automation that improved CI pass rates to over 98%.”

“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”

“Backend/product-minded engineer from Ramp with strong travel-tech experience, having built an end-to-end booking platform integrating multiple external providers, policy enforcement, and reporting infrastructure. They also shipped an LLM-powered personalization workflow using embeddings and Google Gemini that cut trip planning time by 22%, and demonstrated strong production reliability instincts through circuit breakers, health checks, and schema-driven normalization.”

“AI/LLM engineer with strong healthcare domain depth who has shipped production-grade agents for care coordination and clinical workflow automation. Stands out for combining Knowledge Graph RAG, LangGraph orchestration, and rigorous eval/guardrail systems to improve reliability in high-stakes environments, with measurable gains in review time, hallucination reduction, latency, and clinician adoption.”

Rayhaan Mohamed

Screened

Intern Software Engineer specializing in AI and full-stack development

Alpharetta, GA1y exp

“Early-career software engineer with internship experience at CirrusLabs building a voice-enabled CRM workflow that integrated Google Text-to-Speech and GPT-based processing for automated deal creation. Stands out for a reliability-focused approach to AI integrations, including validation, structured logging, prompt refinement, and hardening asynchronous API/UI behavior in real-world application flows.”

“Full-stack product engineer who has built end-to-end apps and internal tools spanning React/TypeScript, Node/Express, and Postgres. Stands out for pragmatic shipping under ambiguity, creating reusable platform primitives like a centralized notification API, and designing safe multi-tenant configurable dashboards with schema validation.”