Vetted Audit Logging Professionals

Zubair Shaik

Screened

Mid-level Full-Stack Developer specializing in AI-driven FinTech platforms

Remote, USA4y exp

“Built and productionized an LLM-powered loan decisioning agent at Bank of America, integrating RAG with microservices to automate creditworthiness assessment and recommendations. Emphasizes real-world reliability and governance (EKS autoscaling, observability, SOC2/PCI security controls), and drove measurable outcomes including 20% faster loan decisions and a reduction in agent failures/fallbacks to under 2% through schema enforcement and confidence-based routing.”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

“Backend/full-stack engineer with deep experience building weather and geospatial data systems at WindBorne, spanning Next.js/TypeScript frontends through PostgreSQL, Redis, Sidekiq, Rails, Rust, and object-storage-backed forecast pipelines. Particularly strong in production reliability work—self-healing jobs, zero-downtime migrations, query/index optimization, and event-driven ingestion architectures that reduce latency and operational waste.”

“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/AI engineer who built an AI icon-generation SaaS backend (Java/Spring Boot, MongoDB) on AWS, including async job processing with idempotency and S3-based result storage to handle traffic spikes. Also shipped applied AI in finance—an end-to-end fraud detection pipeline with risk scoring—and designed a banking support AI agent with strict guardrails, audit logs, and human-in-the-loop escalation.”

“Customer-facing data engineering professional who builds and deploys real-time reporting/dashboard solutions, gathering reporting and compliance requirements through direct stakeholder engagement. Experienced with Google Cloud IAM governance, secure integrations (encryption, audit logging), and fast production troubleshooting of ETL/pipeline failures with follow-on monitoring and automated recovery improvements; motivated by hands-on, travel-oriented customer work.”

“Software engineer who has built both internal developer productivity tooling (a backend API supporting repeatable UI test data/mocking for Dapper) and a personal Go-based LLM workout coach using Gemini and structured logs/config. Emphasizes maintainability and reliability via scalable UI component tagging (Telerik), audit logs, and reproducible Dockerized environments; targeting $160k base.”

“Full-stack engineer with healthcare domain experience (UnitedHealthcare) delivering real-time claims/eligibility dashboards using Spring Boot microservices and React/TypeScript, with strong AWS/Kubernetes DevOps. Demonstrated measurable impact through performance tuning (33% faster retrieval; 45% faster responses during a 60% traffic spike) and HIPAA-aligned security practices. Also built production FastAPI services for high-volume financial transactions with strong testing and observability (95%+ coverage; Prometheus/Grafana).”

“GenAI/ML engineer with hands-on experience building production financial intelligence and document summarization systems at Citibank. Stands out for combining LLM fine-tuning, hybrid RAG, multi-agent workflows, and strong MLOps/observability practices to deliver measurable business impact, including 60% faster analyst retrieval, 31% higher precision, and 99%+ uptime.”

“Junior developer with over a year of experience who is already applying AI-assisted engineering practices in production, including GitHub Copilot, ChatGPT, SonarQube, and OpenAI-based workflows. On a Citibank public portal built with .NET 8 and Angular, they used AI tooling plus CI/CD quality gates to speed delivery and reportedly reduce post-deployment bugs by 30-40% while maintaining strict security and compliance standards.”

“Full-stack engineer with 8 years of experience spanning regulated healthcare IoT and enterprise security SaaS. Has worked on a high-growth healthcare equipment platform and DigiCert's certificate lifecycle management product, combining hands-on React/Node.js development with compliance, auditability, and reliability requirements. Currently seeking a more code-heavy role after moving into a more advisory position.”

“Engineering/product leader who builds business-aligned technology roadmaps and scales pod-based orgs with strong delivery discipline (OKRs, CI/CD, QA automation). Led a SaaS supply-chain application adopted by Fortune 100 customers, citing ~$4M MRR and ~87% gross profit, and has hands-on experience standardizing LLM + cloud/MLOps architectures with security/compliance guardrails. Also created the PISEK methodology and used it to run distributed innovation sprints (e.g., an AI ETA predictor moved from pilot to production).”

“Backend engineer who built the core backend for Air Kitchens’ discovery/booking platform on AWS (Node + Python, DynamoDB, SQS/Lambda), optimizing for fast user-facing APIs and scalable async workflows. Introduced an AI matching service with a deterministic pre-filter + LLM ranking approach to balance latency vs quality, and has hands-on experience with production security (JWT/RBAC/RLS), CI/CD, and blue-green, staged migrations from Django to modular services.”

“Healthcare-focused LLM engineer who deployed a production triage and clinical knowledge retrieval assistant using RAG and LangGraph-orchestrated multi-agent workflows. Emphasizes clinical safety and compliance with robust hallucination controls, HIPAA/PHI protections (tokenization, encryption, audit logging, zero-retention), and human-in-the-loop escalation; reports a 75% latency reduction in a healthcare agent system.”

“Full Stack Engineer with hands-on experience owning customer-facing products end-to-end, emphasizing fast iteration via feature flags and risk-based testing for critical user flows. Built TypeScript/React systems with shared types and clean backend layering, and has microservices experience using RabbitMQ to decouple services and manage scale issues like queue backlogs. Also created an internal dashboard for dev/QA to centralize build/test/deploy visibility and iterated on it through lightweight user research and usage metrics.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”

“Backend engineer with experience building Spring Boot microservices for financial workflows at Fizzle (thousands of requests/minute) and shipping healthcare data validation automation at CVS Health. Demonstrates strong production reliability/performance skills—deep in database tuning (query plans, indexing, caching, denormalization), observability (Prometheus/Grafana), and resilient multi-step workflow design with retries and human-in-the-loop escalation.”

“Full-stack engineer with payments-domain experience from ACI Worldwide who shipped an end-to-end MFA system for payment workflows (React/TypeScript + backend APIs + Postgres), then owned it in production with logging/monitoring and client adoption tracking. Also improved checkout responsiveness across Apple Pay/PayPal flows via React performance profiling, component refactors, and state/network optimizations.”

Sharanya Teegala

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring and AWS microservices

Minneapolis, MN5y exp

“Full-stack engineer with experience at Wells Fargo and Salesforce building regulated, customer-facing financial systems and internal DevOps tooling. Deep in microservices and event-driven architectures (Spring Boot, Kafka/RabbitMQ) with strong CI/CD automation, contract testing, and observability; delivered measurable impact including 60% faster deployments and 40% fewer support tickets.”

“ML/AI engineer with strong production ownership across predictive ML and Generative AI systems. They’ve delivered measurable business impact through real-time churn/drop-off prediction, RAG-based document QA, and scalable LLM optimization, with a consistent focus on reliability, safety, latency, and developer productivity.”

“Backend/platform engineer with experience across Cigna, Cognizant, and a university environment, focused on reliability, distributed systems, and regulated-domain workflows. Stands out for combining Kubernetes/Kafka/AWS infrastructure expertise with a production RAG-based healthcare compliance assistant that cut manual reporting work from 30-45 minutes to under 2 minutes while maintaining strong uptime and data-quality controls.”

“Full-stack and AI product engineer with strong AWS/Snowflake experience who built an internal feature flag platform and helped migrate a cybersecurity insights product into a multi-agent AI chat interface. They report production scale of 1M+ embeddings and 50k+ monthly queries, with outcomes including an 80% reduction in analyst work and dashboard generation in 7 minutes; the work was also featured by Claude and AWS.”

“ML/AI engineer with healthcare payer experience (Signal Healthcare, Cigna) who has shipped production fraud/claims prediction systems using Python/TensorFlow and exposed them via FastAPI/Flask microservices integrated with EHR and Salesforce. Emphasizes operational reliability and trust—Airflow-orchestrated pipelines with data quality gates plus SHAP-based interpretability, A/B testing, and drift/debug workflows—backed by reported outcomes of 22% lower false payouts and 17% higher model accuracy.”

Prasanth Sai

Screened

Mid-level Data Engineer specializing in cloud lakehouse/warehouse pipelines

4y exp

“Data engineer with HCA Healthcare experience building and operating end-to-end AWS-based pipelines for clinical and operational reporting (50–100 GB/day), serving curated data into Redshift/Snowflake for Power BI/Tableau. Emphasizes production reliability (Airflow SLAs/retries/alerting, logging/observability) and strong data quality controls (reconciliations, schema/null/duplicate checks), and has shipped versioned REST APIs to expose warehouse data to downstream systems.”