Vetted Event-Driven Architecture Professionals

“LLM/agent engineer who has shipped multiple autonomous, multi-step agents to production (document-to-SOP conversion, test generation, code generation) using a custom Python DAG orchestrator with persistent state, tool-calling permissions, and structured outputs (Pydantic/JSON Schema). Demonstrates strong production hardening practices—semantic contracts, golden-dataset prompt regression tests, circuit breakers, and multi-level monitoring—and delivered large productivity wins (34 hours of manual writing reduced to ~20 minutes review; ~15–20 engineering hours/week saved).”

Vamshikrishna Bandi

Screened

Senior AI/ML Engineer specializing in Generative AI and agentic multi-agent systems

6y exp

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”

Praveen Nutulapati

Screened

Mid-level Generative AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

New York, NY6y exp

“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”

Harshana Ragipindi

Screened

Mid-level Support/Software Engineer specializing in incident response, automation, and AWS monitoring

USA4y exp

“Built and owned end-to-end travel booking and baggage fee calculation platforms used by both customer support and customers, emphasizing fast iteration with automated guardrails and production visibility. Experienced designing TypeScript/React systems and operating RabbitMQ-based microservices at scale, including disciplined event contracts, idempotent consumers, and schema evolution strategies. Also created an internal real-time troubleshooting/pricing console that replaced fragmented tools and improved support resolution workflows through pilot-led adoption.”

Venu Venkata Surendra reddy Erusu

Screened

Mid-Level Software Engineer specializing in AI/ML and Cloud-Native Microservices

Syracuse, NY4y exp

“Research assistant at Syracuse University who owned a Python/FastAPI analytics backend for user-uploaded large datasets, using S3 streaming uploads and background workers for heavy processing. Has hands-on experience deploying Dockerized Python/Java microservices to AWS EKS with Jenkins-based CI/CD, plus Kafka-based event-driven pipelines and practical migration patterns (dependency mapping, dual-write, reconciliation) to minimize downtime.”

“End-to-end deployment owner for Oracle document delivery/print services in a hospital-like production environment, focused on reliability/performance at scale (thousands of systems). Also describes implementing event-driven RAG/agentic LLM workflows with attention to embeddings/index consistency, latency, and measurable improvements in response relevance and operational efficiency.”

Sunil Parikh

Screened

Executive enterprise architect specializing in cloud, cybersecurity, and platform modernization

Plano, TX26y exp

“Architect with early startup experience (1999-2000) who later worked with Capital One evaluating startup products, strategy, and roadmaps. Brings a structured approach to innovation through market research, competitor analysis, risk assessment, gap analysis, and proof-of-concept thinking.”

Sirisha Maddikunta

Screened

Mid-level Generative AI Engineer specializing in enterprise LLM and healthcare AI solutions

O Fallon, MO6y exp

“Built and owned an end-to-end LLM-powered fraud investigation assistant that automated case summaries and risk analysis, cutting analyst investigation/documentation time by 40%. Stands out for translating RAG concepts into a production-grade internal platform with strong evaluation, monitoring, and reusable Python service architecture that improved both analyst trust and engineering velocity.”

“Built and launched a production self-healing MLOps agent that autonomously diagnosed and fixed model training failures on Kubernetes GPU infrastructure. Combines deep AI infrastructure knowledge with full-stack product ownership, and has delivered measurable impact including 35% less infrastructure waste, nearly 50% less troubleshooting time, and 60% lower LLM API costs.”

“Full-stack engineer with hands-on ownership of a customer-facing advanced performance metrics experience in the Amazon S3 console, spanning React UI, Python/Node services, Redshift/RDS data access, and AWS IaC/CI-CD with CloudWatch/Route53 operational readiness. Demonstrates strong production instincts around resilience (partial failures, multi-region inconsistencies), progressive rollouts/feature flags, and reliable ETL/integration patterns (idempotency, backfills, reconciliation).”

“Robotics software engineer who has built end-to-end pipelines spanning backend/data processing through model interfaces and hardware integration. Has hands-on ROS2 experience building Python nodes and debugging real-time behavior via profiling, publish-rate tuning, and latency fixes, plus experience standardizing multi-robot communication with QoS adjustments. Uses Gazebo simulation and Docker/CI/CD to catch integration issues early and speed iteration.”

“Software engineer focused on reliability and scalable systems: built React/TypeScript dashboards backed by Java/Spring Boot APIs and designed Kafka-based microservices with strong contract/versioning discipline. Known for shipping incremental improvements with tight feedback loops and for creating internal observability tools that streamline on-call and incident diagnosis under high-traffic conditions.”

“Backend-focused Python engineer who built an end-to-end personalized chatbot service integrating Amazon Redshift context retrieval with Amazon Bedrock, including prompt construction and production-grade reliability controls. Strong platform experience deploying containerized services to Kubernetes with GitOps/ArgoCD, plus hands-on Kafka streaming and phased infrastructure migration execution.”

“Backend-heavy full-stack engineer with experience at Intuit (TurboTax Live) and Paytm payments, building and scaling Java/Spring Boot microservices for high-traffic transaction systems. Has hands-on wins improving peak-load performance using Redis/disk caching and Kafka event-driven patterns, plus React/Redux work for web app integration and strong monitoring practices with ELK.”

“Backend/AI workflow engineer with PayPal experience building workflow-driven financial compliance systems (Python/Java, Postgres, AWS/EKS) at thousands of executions/day. Has shipped production LLM-powered document extraction with strict schema/rule validation, auditability, and human-in-the-loop fallbacks, and has deep expertise in reliability (idempotency, locking, state machines) and Postgres performance tuning.”

“Backend/LLM engineer who built a production-critical Amazon Bedrock + RAG correction and compliance layer for employee communications, integrating tightly with existing Spring Boot/AWS microservices to reduce manual review while keeping outputs explainable and auditable. Also designed an event-driven system processing 10M+ events/day (SQS/Lambda/DynamoDB/Elasticsearch) and handled on-call incidents with strong observability and reliability patterns (idempotency, retries, hotspot mitigation).”

“Backend/ML systems engineer with Stripe payments experience who built an asynchronous processing upgrade handling millions of API requests, cutting peak latency ~20–25% while preserving strict financial consistency via idempotency-safe retries and robust validation/fallbacks. Also built scalable ETL pipelines for messy CSV/Excel/API data with strong observability (structured logging/monitoring) and reliability mechanisms.”

Krishna Guda

Screened

Principal Product Engineer specializing in FinTech platforms, experimentation, and AI workflows

Mountain View, CA24y exp

“Fintech product engineer working on a large-scale credit monitoring platform (tens of millions of users) with deep experience in regulated banking integrations, PII security, and step-up/MFA flows. Has shipped customer-facing React/TypeScript experiences driven by Optimizely experimentation and built reliable partner-facing microservices/SDKs on AWS, including resolving production traffic loss caused by edge security (DataDome/CAPTCHA) conflicts with payment providers.”

Abraham Musa

Screened

Senior Solutions Architect specializing in cloud AI infrastructure and security

Union City, NJ9y exp

“Cloud-native architect focused primarily on AWS, with experience designing Kubernetes and AI/ML infrastructure for customers rather than owning day-to-day operations. Particularly interesting for AI platform roles: they described using Amazon Bedrock to analyze Terraform and automatically generate compliant IaC templates and runbooks for new multi-cloud AI environments.”

“Software engineer with hands-on experience building end-to-end internal products at State Farm, spanning React/TypeScript frontends, AWS Lambda serverless backends, and Postgres data layers. Stands out for combining strong systems thinking with product sense: they improved a policy rerate platform with measurable performance gains, built a reusable rerate API adopted across teams, and also shipped an internal LLM chatbot MVP with built-in user feedback loops.”

“Backend/data engineer focused on compliance and statistical processing systems on AWS, building containerized FastAPI services plus event-driven async workflows (Step Functions/EventBridge) with strong reliability patterns (JWT auth, idempotency, structured logging). Has modernized SAS-based batch pipelines into modular Python/AWS services with parallel-run parity validation, and has demonstrated measurable SQL performance wins (40+ min to <10 min) and hands-on incident ownership using CloudWatch-driven detection and prevention.”

“Amazon engineer who owned customer-facing Alexa features and built automation-heavy delivery practices (API/service-level testing in CI/CD) to ship quickly without sacrificing stability. Also built an internal self-service feature management/beta access platform (Angular + Spring Boot + event publishing) that replaced a multi-team ticket workflow with instant, auditable operations, and has deep microservices/Kafka experience with strong observability and reliability patterns.”

“Java full-stack developer who has owned data-intensive, customer-facing and internal web products end-to-end (React/Angular + Spring Boot), including CI/CD and production support. Demonstrates deep microservices experience with RabbitMQ/event-driven architecture, idempotency, DLQs, and compensating logic to maintain reliability and data consistency at scale, plus a track record of replacing spreadsheet-based ops reporting with an adopted real-time internal tool.”

“Backend/platform engineer building HIPAA-compliant, real-time healthcare systems: owned a Python/Flask API layer for an AI-enabled patient engagement and risk scoring service, implemented PHI-safe logging and cross-service auditability, and delivered Kubernetes microservices via ArgoCD GitOps. Also has experience with Kafka streaming pipelines and hybrid cloud-to-on-prem migrations in regulated healthcare/fintech environments.”