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Vetted SQL Professionals
Pre-screened and vetted.
Alex Olson
Junior AI & Full-Stack Developer specializing in generative AI and web platforms
“Recent graduate with internship experience at Bausch + Lomb building Copilot Studio HR chatbots that reduced HR time spent on repetitive inquiries. Strong focus on conversational flow design, prompt-based steering for predictability, and thorough technical/end-user documentation; also building a personal YouTube AI SEO analyzer.”
Snehitha Borra
Mid-level Data Engineer specializing in cloud data platforms and big data pipelines
“Healthcare data engineer with hands-on ownership of claims/member data pipelines on a cloud analytics platform, spanning batch and streaming ingestion (Airflow/Kafka/Spark/Databricks) through serving for reporting. Emphasizes reliability and data quality via embedded validation, schema-drift detection, deduplication, and operational monitoring/incident response, plus pragmatic CI/CD and observability setup in early-stage/ambiguous projects.”
amani mudili
Mid-level Data Engineer specializing in cloud ETL pipelines (Azure, AWS, GCP)
“Data engineer/backend developer who owned end-to-end pipelines and external data collection systems, including API ingestion and large-scale web scraping. Worked at ~50M records/month scale, improving processing speed by 20% and reducing reporting errors by 15%, and shipped a Rust-based internal data API with versioning, caching, and strong validation/observability practices.”
Jayanti Lahoti
Junior Full-Stack Software Engineer specializing in AI and cloud-native systems
“Backend/systems-oriented engineer focused on building production-constrained LLM agent workflows that automate repetitive operator tasks via intent/entity extraction, retrieval grounding, and structured action recommendations with human-in-the-loop review. Emphasizes reliability through deterministic orchestration, strict tool/function schemas, observability, and disciplined evaluation/feedback loops, with strong experience handling messy multi-service operational data and idempotent execution.”
Yasser Ali
Junior AI & ML Engineer specializing in agentic systems and full-stack AI products
“Won a machine learning contest and was placed onto a Kaiser data science team, where they built ML models for hospital bottleneck prediction and resource allocation. They later built and deployed a full-stack LLM-based “data analyst agent” (with custom orchestration plus LangChain/OpenAI Agents experience) that generates analysis code, answers questions, and produces dashboards from uploaded datasets, emphasizing rigorous evaluation sets, robustness, and healthcare security/compliance integration.”
Sandeep Athota
Mid-level AI/ML Engineer specializing in cloud MLOps and production ML systems
“AI/ML engineer at J.P. Morgan Chase who deployed a production financial-risk prediction platform combining CNN/LSTM/gradient boosting on AWS SageMaker, with automated drift-triggered retraining and governance-grade fairness testing. Leveraged SageMaker Clarify plus SMOTE and LLM-generated synthetic data to improve minority-group F1 by 0.12, and communicated results to non-technical risk/ops teams via Power BI dashboards.”
Pedro Cutino
Executive Product Management leader specializing in B2B SaaS, cloud, AI and FinTech
“Operations/product leader with experience integrating product organizations after acquisitions, standardizing Agile/PDLC and RACI to remove ambiguity and enable on-time launches. Hands-on in implementing Jira-based execution frameworks and mentoring exec teams on KPI/OKR operating cadence, including adopting a Product/Engineering/UX triad model to reduce rework and improve customer alignment.”
Pavan Kumar Malasani
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and GenAI
“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”
Junior Software Engineer specializing in distributed systems and full-stack web development
“Software engineer at Cimpress owning end-to-end transactional pages for Pens.com (e-commerce). Built and integrated new discount experiences in a React/TypeScript + Node.js stack, focusing on modular component architecture to reduce tight coupling and avoid breaking existing functionality; prioritizes roadmap work using performance and conversion metrics.”
Yukti Kamthan
Senior Software Engineer specializing in AI/ML and data systems
“Built and shipped production LLM/AI agent systems including an NL-to-SQL query agent with semantic search and Redis-based caching, using schema-aware prompting and threshold validation to reduce hallucinations. Has orchestration experience running ML microservices on Kubernetes and automating event-driven insurance (P&C) workflows (claims/policy + fraud checks), reporting ~60% manual overhead reduction and ~99% uptime, with strong monitoring/drift-detection and business-facing Power BI reporting.”
HEMANTH KUMAR KOTTAPALLI
Mid-level Machine Learning Engineer specializing in GPU-accelerated LLMs and MLOps
“Built and deployed a production LLM-powered decision-support system for supply-chain planners that explains demand forecast changes using grounded retrieval from sales, promotion, inventory, and supplier data. Implemented strict anti-hallucination guardrails and latency optimizations, deployed as a real-time AWS API with monitoring, and reported ~15% forecast accuracy improvement and ~12% supply-chain risk reduction. Experienced orchestrating data/ML/LLM workflows with Airflow, LangChain/LangGraph-style patterns, and AWS Step Functions while partnering closely with non-technical business users via demos and example-based requirements.”
Anishkumar Mahalingam Iyer
Intern Software Engineer specializing in AI/ML infrastructure and applied machine learning
“Interned at Rivian where they built and deployed a production Whisper-based ASR + LLM real-time event labeling pipeline to help autonomous-vehicle engineers diagnose failures and route issues to triage teams. Also built a stateful multi-agent "Code Partner" developer assistant using LangGraph/LangChain (planner/router/coder/critique/tester) with evaluation, adversarial testing, and stakeholder-friendly communication practices.”
Khoa Nguyen
Junior Software Engineer specializing in Python, AWS, and data/ETL systems
“Data/ETL-focused engineer with Amazon experience building and deploying AWS-based pipelines that became the primary source of automated customer feedback insights (processing millions of records daily). Demonstrated strong incident troubleshooting across software/host/network layers using CloudWatch, traces, and metrics, plus hands-on stakeholder and on-site operator collaboration to translate reporting needs into star-schema data models and tailored Python ETL logic.”
Mason Acevedo
Mid-Level Software Engineer specializing in data pipelines, APIs, and ML
“Software engineer whose recent work includes co-designing and building a "Shared Profile" feature for a social event-planning app (Again, Sometime). Previously at Pure Storage, set up Docker-standardized Ubuntu/Python environments to simulate hardware testbeds and support workload/performance regression testing for other engineering teams; no robotics/ROS experience.”
Sachin Reddy Kunta
Mid-Level Backend Software Engineer specializing in payments, fraud systems, and AI agent infrastructure
“Early-career engineer who owned an end-to-end objective assessment/coding contest platform at an edtech startup, using Postgres + S3 and Redis (queues + ZSET) to decouple and scale code submission processing with worker sandboxes. Also implemented idempotency controls and set up monitoring and CI/CD while the rest of the team focused on curriculum.”
Eduardo Diaz
Senior Full-Stack Software Engineer specializing in Python, FastAPI/Django, and Azure
“Backend/data engineer with production experience building real-time IoT telemetry pipelines for wind/solar assets at Siemens (FastAPI on Azure Event Hubs/Service Bus, Cosmos DB + SQL Server) and deploying GPS/fleet telematics microservices on AWS ECS Fargate with Terraform and blue/green CI/CD. Demonstrated strong reliability and performance chops, including a 30s-to-<100ms SQL optimization and owning a Kafka pipeline incident resolved in ~20 minutes.”
Leo Kim
Junior Software Engineer specializing in robotics and full-stack development
“Software Engineer at Armstrong Robotics building multithreaded C++ perception/planning/control software for robotic arms running commercial dishwashers deployed across multiple restaurant sites (up to ~2,000 dishes/day per installation). Strong in production operations: on-call debugging with deep logging/video analysis, rapid hotfixes, Datadog-based monitoring, and a Three.js calibration tool plus large regression test suite to de-risk live deployments.”
Akashreddy Madduri
Senior Backend Engineer specializing in real-time data platforms for FinTech and Healthcare
“Backend/data engineer with experience at JPMorgan building near real-time payment risk and fraud scoring pipelines using Python, Spark Structured Streaming, and Delta Lake, emphasizing auditability, security, and data correctness (dedupe/late events) to reduce false positives. Also led a legacy-to-cloud migration of claims/eligibility data at Cogna with parallel runs, phased rollout, and healthcare-specific validation (ICD-CPT mapping).”
Krishna Kodur
Mid-level Robotics & AI Researcher specializing in human-robot interaction and reinforcement learning
“Robotics software engineer who built an end-to-end mobile manipulation platform (Franka Panda on a Clearpath Ridgeback) for a simulated-kitchen human-robot interaction study with natural speech commands, implemented in Python/ROS. Has hands-on experience integrating diverse sensors (RealSense, LiDAR, biosignals) with deep learning frameworks (PyTorch, Hugging Face) and fine-tuning GPT-Neo, plus simulation (Gazebo) and modern deployment practices (Docker/Kubernetes, CI/CD).”
jiayu Zhao
Junior Quantitative Analyst and Full-Stack Engineer specializing in FinTech and web platforms
“Backend/distributed-systems engineer with AI infrastructure experience who built an AI-driven video generation platform, focusing on an asynchronous FastAPI-based orchestration layer between user APIs and heavy inference services. Strong in production instrumentation and latency/concurrency optimization; actively learning ROS 2 but has not yet worked on physical robotics or ROS-based deployments.”
Huy Hua
Senior Product Designer specializing in enterprise B2B SaaS and AI governance
“Product/UX designer focused on AI governance and GRC, who led end-to-end design of IBM Watsonx.governance by unifying previously siloed products (OpenPages, OpenScale, Factsheets) into a role-based platform for proactive risk detection and audit-ready compliance. Combines deep field research with strong technical fluency (CS degree, SQL, API-aware collaboration) and has shipped award-winning work (iF Gold 2025) with market recognition (IDC MarketScape Leader).”
Atyab Hakeem
Junior Data Scientist / ML Engineer specializing in GenAI and computer vision
“Software engineer who built and deployed OddPulse, a multi-agent LLM-powered continuous financial auditing system aimed at reducing compliance penalties by catching issues before audit cycles. Experienced with TrueAI-based agent orchestration, Airflow on GCP batch workflows, and rigorous evaluation/benchmarking (hit rate/MRR, latency/TTFT, cost) alongside security controls for sensitive financial data.”
Abhay Ravi Kumar
Junior Software Engineer specializing in backend, cloud DevOps, and ML/NLP
“DevOps/data-automation professional with HPE experience who has deployed containerized microservices to AWS EKS and built an end-to-end observability stack (Prometheus/Grafana/CloudWatch via Terraform), reporting zero-downtime deployments and ~40% faster incident response. Also extends Python ETL automation for procurement/operations teams (rules engine, validation, performance tuning) and bridges SAP ERP data into Power BI/Qlik dashboards through close on-site user collaboration.”
Pandari G
Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems
“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”