Vetted PostgreSQL Professionals

Adithi Anugu

Screened

Mid-level Full-Stack Software Engineer specializing in API and data-driven applications

Florida, USA4y exp

“Full-stack engineer with internship experience at Sports Excitement and AI systems work at the University of Florida, combining React/TypeScript, Node.js, and PostgreSQL with applied AI workflow tooling. Stands out for shipping measurable product improvements—30% faster pagination and 40% higher engagement—and for building observability, eval, and human-review layers around AI-assisted data extraction systems.”

“AI/ML-focused software engineer who has built and orchestrated multi-agent systems with separate retrieval, planning, validation, execution, and escalation components. Stands out for combining hands-on experimentation with a strong reliability mindset, using observability, structured logging, tracing, and evaluation to make agentic workflows production-ready.”

“Frontend-focused product engineer from TrialTrace who owned features end-to-end across product decisions, architecture, implementation, and testing. Strong in TypeScript/React applications with complex data visualization and dashboarding, including refactoring core coordinate logic, building filter-state architecture, and shipping fast user-driven features.”

Deepthi Pamisetty

Screened

Mid-level Full-Stack Engineer specializing in FinTech and cloud-native systems

Dallas, TX6y exp

“Full-stack engineer with production experience building AI-powered search and automation systems at JPMorgan Chase and customer-facing product features at Wayfair. Stands out for combining React frontend work with backend microservices, RAG/LangChain AI integration, and cloud-scale performance tuning, including a support chatbot that reduced ticket resolution time by 35%.”

“IBM full-stack engineer focused on document automation for internal and government use cases, building Java Spring Boot services and React/TypeScript UIs that automate XML-to-multilingual PDF workflows. Stands out for turning complex backend automation into usable tools for non-technical operations teams while improving processing speed and reducing manual effort.”

Anshika Bajpai

Screened

Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps

Bloomington, IN4y exp

“Engineer with impactful experience at Palo Alto Networks and Optum, focused on production automation and AI-powered internal tools. Built and owned an end-to-end RAG knowledge system adopted by 1000+ internal users with roughly 75% faster response times, and also transformed a legacy Optum coverage-feed workflow from 500+ minutes to under 3 minutes through data standardization and microservices refactoring.”

“Frontend engineer with deep experience building high-performance, data-heavy web applications, especially geospatial and climate/energy visualization products. Has led React/TypeScript architecture for large interactive UIs and designed Mapbox/MVT-based systems that dramatically reduced payload size and improved responsiveness in production.”

“AIX/IBM Power Systems engineer with hands-on production incident leadership in a regulated banking environment, using deep OS-level tooling to diagnose CPU entitlement and memory pressure issues. Experienced with HMC/vHMC, VIOS, and zero-downtime DLPAR resizing, plus PowerHA/HACMP clustering and validated failover testing. Also drives migration readiness via Bash/Python automation (60% manual-effort reduction) and phased AIX cloud/hybrid cutovers.”

“Backend/founding-engineer-style builder who designed and evolved a near-real-time customer churn prediction platform (FastAPI + AWS SageMaker/Lambda + Redis + MLflow) to enable real-time retention actions, reporting ~18% churn reduction. Demonstrates strong production engineering in secure API design, incremental migrations with data integrity safeguards, and robustness improvements in async pipelines (idempotency, DLQs, retry visibility).”

“Robotics software engineer with autonomous driving lab experience at UCSD, building and optimizing ROS2 perception and control pipelines (camera-based real-time object detection) with a strong focus on low-latency performance and robust message interfaces. Also brings production deployment experience from Hewlett Packard Enterprise, using Docker and Kubernetes for containerized environments and deployment pipelines.”

“Customer-facing technical/product professional with hands-on experience delivering an LLM-driven document processing feature from design to production, including monitoring, logging, and LLM evals. Demonstrates a pragmatic approach to agentic/LLM workflows (using deterministic logic where possible), strong stakeholder alignment, and sales enablement through demos, tutorials, and direct customer calls; has presented to principal engineers (Intuit) and taught coding bootcamps (eBay).”

“Backend/data engineer with strong AWS production experience across containers (ECS) and serverless (API Gateway/Lambda/SQS), plus Glue-based ETL to Parquet for Athena/Redshift. Demonstrates hands-on reliability and security depth (Cognito OAuth2/JWT with JWKS rotation, idempotency/DLQs, monitoring) and measurable performance wins (Redis caching + query tuning), along with legacy-to-services modernization using parallel-run parity and feature-flagged cutovers.”

“Backend engineer with hands-on experience scaling a Python/Flask incident-logging platform processing thousands of daily logs. Strong in performance tuning (PostgreSQL/SQLAlchemy query optimization, partitioning, summary tables) and reliability patterns (Redis caching, Celery background workers, Docker + Jenkins CI/CD), with some multi-tenant data isolation experience via separate DBs/schemas.”

“GenAI/ML engineer from Lucid Motors who built and productionized an LLM-powered RAG diagnostic assistant for manufacturing and maintenance teams, deployed on AWS with Docker/Kubernetes and MLflow. Demonstrates end-to-end ownership from retrieval/prompt design to scalability, monitoring, and workflow integration via APIs, plus production ML pipeline orchestration with Kubeflow (Spark/Kafka + TensorFlow) for predictive maintenance use cases.”

“Built and productionized an LLM-based PDF extraction pipeline for Medicaid policy documents by fine-tuning Gemini Flash 2.0 and deploying via Vertex AI, adding validation/guardrails to improve trust and reliability. Also built and scaled a SaaS platform (cnotes) for cable operators and regularly partners with customers and sales teams through interactive demos, rapid iteration, and real-time workflow debugging.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“Backend engineer at Goldman Sachs who deployed internal LLM-powered utilities to summarize operational logs/tickets, with a strong emphasis on data sensitivity and reliability. Built deterministic workflows with template-based prompts, confidence checks, and rule-based fallbacks, and used monitoring plus failure-rate metrics to tune performance; also has hands-on Temporal orchestration experience for resilient async backend jobs.”

“Built and deployed a production LLM-based real-time stance detection system for social media, fine-tuning LLaMA 3.1 on A100s with DeepSpeed ZeRO/FSDP and iteratively refining data to handle sarcasm and context-dependent meaning. Also has Kubernetes operations experience (Kafka/Logstash/Elasticsearch observability pipeline) and delivered an OCR automation project during a Worley India internship that saved 20+ hours/week for on-site energy safety stakeholders.”

“Software engineer with hands-on experience improving performance and reliability in financial workflows (settlements/loan processing), spanning React/TypeScript and Angular frontends plus Spring Boot microservices. Has delivered measurable latency improvements using PostgreSQL optimization and Redis caching, and has operated Kafka-based systems at scale with idempotent processing and backoff/retry strategies while iterating internal ops tooling with support/finance teams.”

“Software engineer/product-focused builder who has delivered customer-facing dashboards (React/TypeScript + Spring Boot) and microservices using RabbitMQ, emphasizing safe, fast iteration with CI/CD, feature flags, and monitoring. Also built an internal monitoring/reporting tool adopted by ops/support by involving users early and iterating based on feedback.”

“Built and deployed a production LLM/RAG chatbot at Wells Fargo for securely querying regulated financial and compliance documents, emphasizing low hallucination rates, explainability, and strict governance. Experienced with LangChain multi-agent orchestration plus Airflow/Prefect pipelines for ingestion, embeddings, evaluation, and retraining, and partnered closely with compliance/operations to drive adoption through demos and feedback-driven retrieval rules.”

“Built and owned major parts of a real-time distributed AI fraud-detection pipeline (ingestion, inference microservice integration, and automated action layer), optimizing latency and observability and reducing false positives by ~35%. Understands ROS/ROS2 concepts (nodes/topics/services) and planned hands-on ramp-up via ROS2 pub/sub exercises and Gazebo simulation, but has not worked on physical robots or ROS in production.”

“Software engineer working on an e-commerce platform, currently building a RAG-based recommendation system with a team new to the technology. Has delivered an end-to-end React/TypeScript website for a local car dealer and built an internal "encryption as a service" tool to secure sensitive data across repositories and through release/UAT, with experience debugging microservices integration issues.”