Vetted Java Professionals

“Built and productionized an autonomous research agent (AutoGPT) in a Docker/Kubernetes environment with Pinecone-based long-term memory and custom Python tools for analysis, visualization, and report drafting. Implemented layered guardrails (prompt templates, automated validation, self-critique loops, and monitoring) and achieved ~25% reduction in manual report generation time while scaling the workflow to support multiple concurrent users.”

“Built and productionized an NLP-powered customer support assistant at JPMorgan Chase for digital banking, focused on reducing response time for repetitive client queries. Strong in real-world AI deployment challenges—sensitive data handling, low-latency FastAPI services, and AWS/Kubernetes operations with CI/CD—plus a metrics- and guardrails-driven approach to reliable AI workflows.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“Full-stack engineer (backend-led) with recent experience building enterprise workflow orchestration and billing/payment platforms at Intuit using Java/Spring Boot (WebFlux), Kafka, Postgres/Redis, and React/TypeScript. Has operated at high scale (reported ~1200 RPS during month-end billing) and focuses on event-driven microservices, real-time UI updates via streaming, and disciplined API evolution with contract testing.”

“Backend engineer with experience at Comcast and in healthcare/pharmacy automation (PrimeRx), building Python/Flask services that orchestrate large-scale batch workflows (Airflow) and high-throughput event processing (Kafka). Demonstrated measurable performance wins (cut provisioning latency to ~150–200ms) and strong multi-tenant isolation strategies (Postgres RLS, partitioning), plus practical integration of ML model outputs into production systems with validation and fallback controls.”

“Engineering leader (Credit Karma/fintech) who built internal developer-platform frameworks at near open-source scale, including a unified key management system migrating ~450 services from on-prem HSM to a centralized KMS. Known for driving large cross-team migrations with strong safety mechanisms (canaries, shadow reads, rollback toggles) and measurable DX/ops improvements (60% fewer provisioning requests; 40% faster time-to-first-secret).”

“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”

“Engineering leader who proposed and delivered a new API-based document management platform to replace a vendor-dependent system, improving latency by ~1s and availability to 99.9% while migrating legacy data. Also drove Python-based automation of ~12 workflows via third-party API integrations and led an SSO/auth integration focused on backward compatibility and high login success rates.”

“Built a lightweight internal JavaScript analytics tracker capturing user interactions (clicks, page views, custom events) with debounced batching, automatic session tracking, and offline event caching via a localStorage-backed append-only queue. Demonstrates practical performance optimization mindset (profiling, memoization/caching) and React performance tuning.”

“Backend/platform engineer from Fanatics sportsbook core team with deep experience in real-time ingestion systems (Kafka) and high-throughput performance optimization. Delivered an 87% latency reduction on a Java API handling hundreds of thousands of updates per second, and improved reliability of shared internal libraries via deterministic recovery logic, strong testing, and feature-flagged rollouts.”

“Founding engineer for an AI product (“world’s first funny AI”) who designed and implemented the full-stack architecture (React/TypeScript + Node) and migrated production from Vercel to AWS. Shipped a Lambda-based image pipeline that eliminated lag/missing images and brought page load times to under a second, and has hands-on experience integrating multiple LLM providers (OpenAI, Claude, Gemini, Grok) with structured-output and self-check reliability techniques.”

“Backend engineer who has led major platform evolution to cloud-native microservices (Spring Boot on AWS with Terraform) and built scalable, secure FastAPI APIs. Demonstrates strong production rigor with metric-driven validation, canary/phased rollouts, and incremental migrations using shadow traffic/feature flags/parallel writes—achieving faster deployments, fewer incidents, and zero-downtime traffic spikes and migrations.”

“Software engineer in the mortgage/financial services domain (Freddie Mac) who builds end-to-end loan origination and credit risk capabilities using Spring Boot microservices, Angular dashboards, and data pipelines. Delivered measurable impact (30% reduction in underwriting turnaround time) and emphasizes production reliability/compliance with strong guardrails, observability, and evaluation loops for risk scoring systems.”

“Engineering leader/player-coach who led a high-stakes 0→1 in-house usage-based billing/rating platform to replace a third-party system, saving millions and protecting 30M+ in revenue. Deep experience in event-driven microservices on AWS (Lambda/SQS/DynamoDB), billing correctness, migrations, and incident response, while scaling teams through hiring, coaching, and lightweight execution processes.”

“ServiceNow engineer who built and launched a production LLM-powered ticket resolution/knowledge assistant using RAG (LangChain + Hugging Face embeddings + vector search) integrated into internal support dashboards via REST APIs. Optimized the system from ~6–8s to ~2–3s latency while improving usability with concise, cited answers and guardrails (grounding + similarity thresholds), delivering ~30–35% reduction in manual ticket investigation effort.”

“Uber engineer who has owned internal products end-to-end across backend (Spring Boot microservices, MySQL) and frontend (React), including performance optimization and secure JWT-based auth. Also shipped a production internal RAG/embeddings LLM support assistant over policy docs and support tickets, with guardrails (confidence thresholds, human review) and an evaluation loop that directly reduced hallucinations.”

Allan Farinas

Screened

Senior Full-Stack Software Engineer specializing in Python and AWS

West Covina, CA11y exp

“Backend/data engineer who has built production Python microservices (FastAPI) and AWS-native platforms for event ingestion and analytics, combining ECS/Fargate + Lambda with CloudFormation-driven environments and strong secrets/IAM practices. Experienced modernizing legacy logic with parallel-run parity validation and safe phased cutovers, and has demonstrated measurable SQL tuning wins (20–30s down to 1–2s) plus incident ownership in Glue/Step Functions ETL pipelines.”

Hardik Goyal

Screened

Senior Integration Developer specializing in enterprise automation and data integration

United States4y exp

“Frontend-focused engineer with experience building and optimizing React-based dashboards and reusable component libraries in a multi-team, internal open-source-style environment at Merck (ClearSight Forecasting Dashboard). Also handled production user issues on a live streaming platform (GameSee.tv) and built a financial application from scratch at Manipal Business Solutions, owning backend services, middle-tier APIs, and third-party integrations.”

Ethan Lam

Screened

Junior Software Engineer specializing in data platforms and full-stack development

Toronto, Ontario3y exp

“Software engineer with Warner Music Group experience owning and shipping analyst-facing data products (marketing/streaming data dashboards) end-to-end with high adoption through continuous stakeholder feedback. Also builds side projects with TypeScript/React and domain-driven API design, emphasizing flexibility (including swapping databases mid-development) and pragmatic microservices reliability patterns (logging, timeouts, retry backoff).”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

“Senior software engineer (5+ years) from Walmart Global Tech who owned and operated high-scale supplier inventory submission systems, including a microservice handling submissions up to 500k items and a data platform processing ~10TB/day. Strong in AWS/Kubernetes (EKS), Kafka/Spark streaming + batch pipelines, and production operations (on-call, metrics/alerting), with demonstrated performance wins (30% faster responses, 50% faster processing).”

Vineeth Reddy Vallapureddy

Screened

Mid-level Full-Stack Software Engineer specializing in backend microservices and enterprise AI tools

Redwood City, California5y exp

“Backend/platform engineer with experience across C3.ai (supply chain demand planning) and Amdocs (telecom), working on large-scale data systems and microservices. Has driven first-time adoption experiments of Snowflake + Spark to handle billion-record workloads, built Jenkins-to-Kubernetes delivery pipelines with Nexus artifact management, and implemented Kafka streaming between microservices with HA and retry/error-handling patterns.”

Thomas To

Screened

Mid-level Full-Stack Engineer specializing in AI/ML data platforms for biotech and FinTech

Emeryville, CA6y exp

“AI/ML full-stack practitioner in a small-scale manufacturing/lab operations environment who deployed a production ML system to improve blood cell order fulfillment by predicting yield/success from donor characteristics. Experienced building custom multi-agent orchestration (Python, LangChain/LangGraph, MCP) and balancing reliability, data quality constraints, and token/ROI economics while communicating tradeoffs to VP-level business stakeholders.”