Vetted SQL Professionals

Muaaz Syed

Screened

Mid-level AI/ML Engineer specializing in NLP and conversational AI

Richardson, TX4y exp

“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”

Ramcharan Reddy

Screened

Mid-level Full-Stack Developer specializing in FinTech platforms and cloud-native microservices

Texas, USA6y exp

“Backend engineer focused on AI-enabled systems, having built a production-style RAG pipeline (vector search + LLM) exposed via Python/Flask endpoints with strong observability and hallucination-reduction techniques. Demonstrates deep performance work in PostgreSQL/SQLAlchemy (5x faster analytics queries) and high-throughput optimization using Celery + Redis (800ms to 120ms latency, 3x throughput), plus schema-per-tenant multi-tenancy with tenant-aware middleware and logging.”

Rohan Varma Bandari

Screened

Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG

USA4y exp

“Built production LLM + hybrid RAG and multi-agent orchestration systems at Wells Fargo to automate complaint document/audio transcript understanding and categorization, addressing vocabulary drift via embedding + vector index updates instead of frequent retraining. Strong in LLM workflow reliability (testing/benchmarks/observability) and stakeholder-facing delivery with explainability (citations/SHAP-style justifications) and Tableau dashboards.”

Lauren Stein

Screened

Executive Chief of Staff specializing in operations, HR, and GTM enablement for SaaS

7y exp

“Chief of Staff with strong cross-functional leadership across go-to-market and product/engineering, focused on revenue-aligned prioritization and executive operating cadence. Implemented integrated Looker reporting to connect product usage/adoption with revenue, retention (GDR/NDR), churn risk, and upsell—enabling faster executive decision-making and clearer accountability.”

Sreeja Venkatakrishnan

Screened

Mid-level Full-Stack Developer specializing in Java/Spring and modern JavaScript frameworks

Virginia, USA4y exp

“Full-stack engineer with hands-on experience building real-time applications (Socket.io chat app) and data-heavy systems in banking/loan management. Comfortable across React and backend services (Spring Boot/Node), with a focus on scalable API design, database performance (indexing/pagination/caching), and deployment via CI/CD and cloud infrastructure.”

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

Shivani Sharma

Screened

Mid-Level Software Engineer specializing in Cloud, DevOps, and MLOps

Boston, MA3y exp

“Built and productionized a recommendation system from notebook prototype into a low-latency, scalable Cloud Run service using Docker, FastAPI, Terraform, CI/CD (GitHub Actions), and MLOps tooling (Vertex AI, MLflow). Experienced diagnosing real-time workflow issues using structured logging/ELK and GCP metrics, including resolving intermittent 504s by fixing unbounded SQL and adding caching. Also partners with sales/customer teams (Wasabi) to deliver tailored demos, troubleshoot, and drive onboarding/adoption.”

Nagaveda Sai Kumar Reddy Gajjala

Screened

Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud integrations

Richardson, TX4y exp

“Backend engineer with enterprise SaaS experience (Zoho) who owned an end-to-end cloud integration between Endpoint Central and ServiceDesk Plus, redesigning device onboarding across 64+ scenarios and building a fault-tolerant sync engine that recovered 100% failed transactions. Also built and operated production systems across the stack—FastAPI services with strong testing/observability, React+TypeScript portals, PostgreSQL performance tuning, and AWS deployments with real incident response (RDS CPU saturation resolved with zero downtime).”

Naga Chandra mouli Thammineni

Screened

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

Hyderabad, Telangana, India2y exp

“Software engineer who built and owned a centralized Asset Management platform at Hyundai Mobis used daily by multiple internal teams, delivering everything from UI/TypeScript React frontend to Spring Boot services, databases, deployment, and post-release support. Experienced in scaling microservices with Kafka-based async workflows, improving data consistency/real-time updates, and implementing reliability patterns like standardized API contracts, retries, DLQs, and targeted caching.”

Ojasmitha Pedirappagari

Screened

Mid-level AI Engineer specializing in LLMs, RAG, and agentic platforms

Jersey City, NJ5y exp

“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”

Srilekha Jakkula

Screened

Senior Data Engineer specializing in scalable data pipelines and API-driven data services

Chicago, IL5y exp

“Data engineer focused on building scalable, reliable end-to-end data pipelines and backend REST data services, spanning API ingestion plus batch/stream processing with Airflow, Kafka, Spark/PySpark, and SQL. Emphasizes strong data quality validation, monitoring/fault tolerance, and performance tuning for large datasets, with experience deploying in cloud environments using containerization and CI/CD.”

Gitesh Sagvekar

Screened

Mid-level Full-Stack Developer specializing in cloud-native healthcare applications

USA4y exp

“Full-stack engineer with recent experience at Amgen building an internal healthcare data validation/transformation and workflow automation service: Python/FastAPI backend with REST APIs plus a React UI, designed around a canonical contract-first model to handle inconsistent upstream data. Operates production systems on AWS (EC2/ELB/S3/CloudFront) with strong focus on observability (structured logs, correlation IDs) and safe CI/CD-driven migrations; also has experience shipping quickly in ambiguous environments at TCS.”

Ryan Echols

Screened

Senior Workforce Analytics & WFM Leader specializing in contact center operations

Marietta, GA8y exp

“Operations-focused team lead currently managing 20 coordinators, with strong workforce management experience spanning forecasting, scheduling, KPI/staffing reporting, and executive-facing data presentations. Led a cross-functional Salesforce implementation and redesigned forecasting/workflow to support a newly created internal-promotion department, improving flexibility in coverage planning.”

“Backend/real-time systems engineer transitioning into robotics software: building ROS 2 fundamentals (pub/sub, custom messages, launch files) and experimenting with Nav2 + SLAM in Gazebo/RViz. Demonstrated practical debugging by tuning costmaps/planners and analyzing topic latency to stabilize simulated navigation, and has experience integrating telemetry pipelines and REST-based external interfaces.”

“Backend/microservices engineer (Java/Spring Boot, Kafka, Angular microfrontends) with Teradata experience building distributed analytics/query routing platforms and delivering 20–30% latency reductions through event-driven redesign and reliability hardening. Also built and shipped an end-to-end multimodal medical imaging AI feature (LLaVA/Mistral 7B + LoRA) with production guardrails like confidence-based human review, drift monitoring, and audit logs.”

“Backend engineer who built a real-time, event-driven alerting platform (Java/Spring Boot, Kafka, MongoDB) processing millions of events per day on AWS (Docker/Kubernetes), including hands-on performance debugging of Kafka consumer lag at peak. Also shipped an end-to-end LLM-based alert summarization feature and designed a multi-step incident triage agent workflow with retries and human-in-the-loop escalation.”

“Full-stack engineer with production experience across React/TypeScript, Node/Express, and Java/Spring Boot, operating containerized systems on AWS (EKS/ECS/EC2/RDS/S3) with strong observability (CloudWatch/Grafana). Notable for fixing a real checkout/order-placement failure end-to-end by adding frontend submission guards and backend idempotency with Redis + Kafka deduplication, then validating impact via technical metrics and business KPIs. Has also built Kafka-based integrations/pipelines with robust retry/backfill/reconciliation patterns in retail and banking contexts.”

“Strategic sourcing/procurement professional with experience at Gumpro and Micron leading indirect (MRO/CapEx) and critical component sourcing initiatives. Uses structured category strategy (spend/Kraljic/Pareto), RFX, risk management, and TCO/should-cost models; reports $10M+ savings and lead-time reduction from 60 days to 2 days while mitigating geopolitical/tariff exposure and strengthening supply resilience.”

“Backend engineer who architected and owned a mission-critical outage management/decision-support platform, replacing a legacy system that failed under load. Emphasizes auditability, deterministic validation, and server-side concurrency controls (section locking, scoped autosaves), plus redundancy/load balancing and monitoring to keep the system stable for 24/7 operations handling 1,500+ weekly outage events.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“DXArts PhD researcher and recent UW capstone contributor building autonomous robotics systems with ROS2 (SLAM Toolbox, Nav2) and Gazebo simulation. Currently focused on integrating a 9-DOF SparkFun IMU with motor controls on Raspberry Pi, and developing OpenCV ArUco-marker tracking for an automated BlueROV that can locate and retrieve underwater targets in collaboration with mechanical engineering.”

“Built a healthcare app backend and supporting product pieces from scratch for Maverick Health—covering database schema, API structure, Node.js implementation, and UI design in Figma—while targeting 10,000 patients and keeping AWS run costs to ~$20–$30/month. Shipped an Android closed beta on Google Play and handled real-world launch hurdles like privacy policy compliance and push notification infrastructure.”

“Performance marketer focused on scaling high-spend ecommerce paid media, with hands-on ownership of a ~$150K/month Google Ads account for Solawave. Known for disciplined account restructuring, feed optimization, and controlled testing across Google/Meta/TikTok—driving ROAS improvement from ~2.2 to ~5 in under three months while keeping spend predictable and scalable.”

“Software engineer/product owner who has led end-to-end delivery of AI and content-management platforms, including building RAG-based reliability improvements and migrating fragile systems to containerized AWS ECS/Kubernetes with Terraform-managed CI/CD. Experienced designing event-driven microservices (SQS/SNS/RabbitMQ), scaling queue consumers with autoscaling, and creating internal Python tooling to standardize data connectors (e.g., BigQuery/Airtable/internal APIs) to speed iteration.”