Vetted Datadog Professionals

“Full-stack engineer with 6+ years of experience building high-scale internal products and AI-powered workflows, including a U.S. Bank payment operations dashboard handling 500k+ transactions and real-time analyst collaboration. Stands out for true end-to-end ownership—from React/TypeScript frontend architecture to Node/Spring services, PostgreSQL/Redis optimization, Kubernetes deployment, and Datadog monitoring—plus measurable impact on adoption, latency, and analyst efficiency.”

“Fullstack engineer from Prudential who built a workflow automation platform for internal service reps, combining Angular/React frontends with NestJS, GraphQL, Kafka, MongoDB, and Redis. Stands out for translating ambiguous business problems into scalable metadata-driven systems, validating architecture through hands-on POCs, and delivering a measurable 40% reduction in transaction handling time.”

“Engineering/technology leader who stays hands-on while driving executive-level roadmap execution, with deep experience modernizing cloud-based LIMS/LIS platforms and building AI-driven lab analytics. Led a monolith-to-microservices cloud migration with containerization and CI/CD, and delivered a reported 30% reduction in lab turnaround time while strengthening compliance.”

“Front-end engineer who built a sophisticated browser-based registration platform for the JPMorgan Corporate Challenge, serving global users and handling team-based registration complexity, concurrency, and performance. Stands out for combining React/TypeScript UI engineering with accessibility improvements, UX polish, and production-minded reliability practices like Datadog monitoring and Kubernetes health checks.”

“Built and owned an AI-powered real-time financial fraud detection and monitoring platform end-to-end, spanning product decisions, backend architecture, frontend dashboards, deployment, and production support. Their work scaled to 120M transactions/day and materially improved fraud detection accuracy from 78% to 94%, showing rare breadth across distributed systems, observability, and React-based operational analytics.”

Supreet Purthpli

Screened

Mid-level Software Engineer specializing in FinTech and cloud-native systems

San Francisco, CA4y exp

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

“Backend engineer who designed and evolved an AWS-based event-processing system in Python/PostgreSQL, achieving a 60% p95 latency reduction while improving reliability during traffic spikes. Led a zero-downtime migration from a monolithic Django app to FastAPI microservices using feature flags, strong testing, and cross-team coordination, with production-grade observability (Prometheus/Grafana/CloudWatch) and security (JWT/OAuth2, RBAC, Postgres RLS).”

“Backend/data engineer who builds production FastAPI microservices and AWS serverless/Glue pipelines for SMS analytics and marketing segmentation. Led a legacy batch modernization into modular services (FastAPI + Glue/Athena + ClickHouse) using shadow-mode parity checks, feature flags, and incremental rollout. Demonstrated measurable performance wins (12s to sub-second SQL; ~40% CPU reduction) and strong incident ownership with proactive schema-drift prevention.”

“Data engineer focused on building reliable, production-grade pipelines and external data collection systems on AWS (S3/Lambda/SQS/Glue/EMR) using PySpark/SQL, serving curated datasets to Snowflake/Redshift for finance and fraud teams. Has operated a large-scale crawler ingesting millions of records/day with anti-bot tactics, schema versioning/quarantine, and CloudWatch/Datadog monitoring, and also shipped a versioned REST API with caching and query optimization.”

Keerthi Kalluri

Screened

Senior Full-Stack & GenAI Engineer specializing in healthcare and financial services

6y exp

“Built and deployed a production LLM-powered customer support assistant using a RAG backend in Python, focused on deflecting repetitive Tier-1 tickets and reducing resolution time. Demonstrates strong production engineering instincts around reliability (confidence scoring + human fallback), scalability/cost optimization (multi-stage pipelines), and workflow orchestration/observability (LangChain, custom DAGs, structured logging, step metrics).”

“Backend engineer with healthcare and financial services experience (Humana, PNC) who owned production-grade, high-volume ingestion-to-API pipelines end-to-end in C#/.NET and SQL. Strong focus on data quality, handling out-of-order/partial upstream records, and improving reliability/observability via structured logging and telemetry, plus significant SQL performance tuning to reduce peak-load issues.”

Brendan Dwyer

Screened

Senior AI/ML Engineer specializing in GenAI and cloud platforms

Colorado Springs, CO10y exp

“ML/AI engineer with hands-on experience turning research-style RAG concepts into production underwriting systems at Prudential Financial. Built an internal document intelligence assistant end-to-end with strong monitoring, safety, and evaluation practices, driving a 38% faster review process and 31% better retrieval accuracy. Also improved platform engineering at VivSoft by standardizing Python-based ML deployment across 60+ models.”

“Growth-stage startup engineer with strong end-to-end ownership across React, Java/Spring Boot, MySQL, and cloud deployment. They led workflow automation products that replaced manual operational processes, improved efficiency and reliability, and evolved the platform into a more configurable B2B workflow system shaped by user feedback.”