Vetted Event-Driven Architecture Professionals

“Built production-grade Python systems in a medical/imaging context, including an image feature extraction and survival prediction microservice with strong testing, validation, and observability practices. Also developed a Playwright-based autonomous job application agent that handled dynamic UIs and anti-bot challenges with stealth tooling, proxies, and human-in-the-loop escalation.”

Akhil Kunala

Screened

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

Seattle, WA5y exp

“Amazon engineer with 5+ years of experience who built an AI-assisted log investigation and triage workflow that cut debugging time by about 30% during on-call incidents. Combines observability tooling like CloudWatch and Splunk with Python, prompt engineering, and RAG-based diagnostics, and has practical experience orchestrating agentic AI workflows with a strong human-in-the-loop reliability focus.”

Prakash Bhanu

Screened

Director of Software Engineering specializing in cloud, platform, and FinTech systems

Sunnyvale, CA22y exp

“Senior software engineering leader with broad 0-to-1 product experience spanning web apps, microservices, monoliths, messaging platforms, ML/AI products, and large-scale distributed systems. Notable examples include building a payroll/finance product for cast and crew, a distributed messaging platform, and a Walmart application deployed across multiple CDNs and clouds handling hundreds of TPS, with personal ownership across architecture, design, coding, and support.”

“Backend-focused engineer with experience at AWS building a global alarm processing platform (Python, Lambda/SQS/DynamoDB) handling traffic spikes and reliability issues; resolved duplicate alerts and latency under load by fixing hot partitions and enforcing idempotency. Previously at Cognizant, built Java/PostgreSQL backend workflows for healthcare dashboards using pre-aggregated summary tables, strong SQL optimization, and state-driven job orchestration with ELK-based observability and production guardrails.”

“Built end-to-end AI features across both fitness and insurance domains, including a full-stack personalized workout recommendation system and a production RAG-based insurance QA assistant at Relevance Labs. Stands out for combining backend/distributed systems skills with practical LLM architecture, evaluation, and risk-aware human-in-the-loop design; notably reduced unnecessary LLM calls by 40% while improving latency and answer reliability.”

Amre Amer

Screened

Senior Unity XR Engineer specializing in mixed reality, ML, and robotics

Reno, NV14y exp

“Unity/C# developer with unusually broad hands-on experience spanning gameplay systems, VR/Quest hand-tracking ML, low-latency LAN multiplayer, and AR enterprise training tools built from CAD data. They’ve delivered measurable performance wins like 10x smaller animation files and 72Hz runtime control, and have owned end-to-end technical features from data pipeline design through stakeholder buy-in and shipped prototypes.”

“Staff software engineer at Indeed specializing in high-scale financial and invoicing platforms, including a major 8-team migration that moved invoicing for $6B in revenue to a new accounting vendor. Combines deep distributed-systems experience with pragmatic technical leadership and has also built AI bots that cut SDLC lead time from 20 days to under 7.”

“Cloud infrastructure/product engineer with end-to-end ownership of cloud-native storage/observability products, including taking an internal CMS to Google Cloud Marketplace and scaling to ~40,000 deployments. Strong in Kubernetes-based platforms (Operators, microservices, RabbitMQ) and performance/scalability work (e.g., 200% cluster capacity increase) plus internal tooling that materially improved SRE/QA debugging and release velocity.”

“JavaScript/React-focused engineer with meaningful open-source impact: redesigned cache key normalization for a client-side data fetching/caching library using deterministic hashing, added robust test coverage, and collaborated closely with maintainers through GitHub PRs/issues. Also drives measurable runtime improvements by profiling hot paths, refactoring core abstractions, and validating with benchmarks/load tests; has taken ownership of unowned initiatives like improving relevance/ranking in an internal search platform.”

“Backend-leaning full-stack engineer with e-commerce and analytics experience who modernized synchronous order workflows into a Kafka-based event-driven architecture (Java/Spring Boot) to reduce checkout latency and peak-traffic failures. Has built production FastAPI services with JWT/RBAC and strong testing/observability, delivered React+TypeScript reporting dashboards, and handled AWS scaling incidents end-to-end (RDS read latency mitigated with read replicas and query tuning).”

“Product-focused full-stack engineer building a GenAI-powered case summarization workflow for a telemetry dashboard, spanning React/TypeScript UI (confidence indicators, reasoning traces) and Python/FastAPI backend with caching to control LLM latency/cost. Has operated services on AWS (ECS Fargate, RDS Postgres, S3) and Kubernetes, and has hands-on experience resolving real production latency incidents through query/index optimization and caching.”

“Full-stack engineer building real-time internal banking operations dashboards (Java/Spring Boot microservices + React/TypeScript) with Kafka-based streaming and post-launch performance optimizations. Also shipped a production internal AI support assistant using RAG (Confluence/PDF/support docs ingestion, embeddings + vector DB retrieval) with guardrails, evaluation loops, and observability to reduce hallucinations and prevent regressions.”

“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).”

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.”

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.”

“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.”

“Director-level Senior Manager of Software Engineering at Discover with roughly a decade in web application engineering leadership, focused on modernizing legacy banking platforms into cloud-native SPA architectures. Stands out for combining large-team people leadership with hands-on technical depth in architecture, debugging, and prototyping, including GenAI experimentation and high-scale customer-facing migrations.”

Mos Fard

Screened

Senior Distributed Systems Architect specializing in backend platforms and FinTech

Scottsdale, AZ12y exp

“Full-stack engineer who built an AI-powered visual product discovery feature end to end across web, mobile, backend, and ML integration. Particularly strong in TypeScript-first monorepo architecture, serverless AWS microservices, and productionizing computer vision/LLM pipelines with monitoring, prompt refinement, and human-in-the-loop quality controls.”

“Architecture leader from Disney who managed system, AI, and data architects while staying hands-on in solution design. Has experience building LLM-based video advertising products, designing Kafka-based real-time data architectures, and using MVP/POC approaches to align product and executive stakeholders.”