Vetted AWS Professionals

“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.”

Srinivas Vasudevan

Screened

Junior Software Engineer specializing in distributed systems and FinTech

Durham, NC3y exp

“Built an end-to-end payment fraud monitoring dashboard with a React/TypeScript frontend, GraphQL backend, Redis hot path, and a production RAG chatbot, while solving real-time latency and scaling issues. Also shipped an OCR system on AWS EKS for a live manufacturing line at Troxler, improving production accuracy by 15% with custom preprocessing and model tuning.”

“Senior product leader with a track record of transforming legacy and labor-intensive products into AI-native, high-growth platforms across automotive SaaS, legal tech, enterprise software, and UCaaS. Most notably, they rebuilt CallRevu’s call analysis engine to replace human review with AI, cutting processing costs by 93% and reducing turnaround from 30 minutes to under 3 seconds, while also launching a new adjacent product line.”

“Backend-focused AI engineer with enterprise experience building startup-style internal products at JPMorgan Chase. He helped create an AI-powered financial research platform for analysts, leading retrieval and multi-agent orchestration work that cut research prep from hours to under 20 minutes while scaling across large volumes of SEC filings and earnings transcripts.”

Sanjay Santhanam

Screened

Mid-level AI Software Engineer specializing in LLMs and FinTech data systems

San Jose, CA4y exp

“Backend/AI systems engineer focused on productionizing agentic document-processing workflows for large financial PDFs. They describe owning deployments end-to-end, combining Python, Redis, LLM function calling, RAG/ReAct-style orchestration, and strong reliability practices to deliver 80% faster processing, reduce parsing errors from 12% to ~1%, and sustain 99.9% uptime in high-concurrency environments.”

“Customer-facing technical pre-sales operator with experience spanning conversation intelligence, fraud/AML, martech personalization, and SEO SaaS. Stands out for turning complex enterprise evaluations into credible implementation paths, with measurable impact including a 33% reduction in deployment timelines, $5.4M+ ACV account support, and direct influence on seven-figure pipeline and deal outcomes.”

“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.”

“ML engineer with production experience at Goldman Sachs and Medtronic, focused on real-time AI systems in fraud detection and healthcare. Brings a rare mix of backend ML infrastructure, MLOps, and product-minded UX thinking, including dashboard and API design that made complex model outputs usable for analysts and clinical users.”

“Frontend engineer with experience building real-time trading operations dashboards in React and TypeScript, focused on dense operational data, performance tuning, and maintainable component design. They have production experience optimizing large-data UIs and academic exposure to map-based weather applications using Google Maps and Mapbox, while currently requiring future H-1B sponsorship.”

Michael Yudanin

Screened

Director-level Engineering Leader specializing in AI/ML and enterprise software

San Ramon, CA19y exp

“Engineering leader with 20+ years of experience who still codes daily, currently directing AI-driven knowledge management systems at eGain. He stands out for combining large-scale people leadership with deep technical ownership of LLM/NLI pipelines and for a track record of turning complex operational problems into high-impact systems, including a manufacturing platform that boosted throughput 4x and expanded across 11 plants.”

“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.”

“Built and productionized an LLM-powered clinical assistant at a healthcare startup, re-architecting a prototype into a robust RAG system on AWS with guardrails, citations, monitoring, and automated tests for clinical reliability. Works closely with clinicians to convert workflow feedback into evaluation criteria and iterative system improvements, and has hands-on experience debugging agentic systems in real time (including during live client demos).”

“Backend engineer with hands-on experience building Flask REST APIs (auth, CRUD, S3 media uploads) and driving measurable Postgres/SQLAlchemy performance gains (p95 reduced to 200–400ms by eliminating N+1s and switching to keyset pagination). Implemented multi-tenant isolation with strict tenant scoping plus Postgres RLS, and built an OpenAI-powered quiz generation pipeline using queued workers, structured JSON outputs, and Celery/Redis optimizations to stabilize high-throughput workloads.”

“CMU candidate with hands-on experience taking LLM concepts from research prototypes toward production-ready designs (structured outputs, guardrails, failure-scenario evaluation). Also partnered with sales/customer teams at Mazecare to drive adoption with Dontia Alliance (largest dental clinic chain in Singapore) and engaged Singapore government stakeholders, bridging clinical workflow needs with IT security/integration concerns.”

“Strategy & operations leader with senior roles at OnDeck, BlockFi, and Protocol Labs who owned founder-level initiatives end-to-end. Notably launched the first crypto credit card, driving 150k first-year sign-ups (vs 30k target) and boosting adoption of other offerings, and later cut time-to-hire from ~6 months to ~6 weeks while rebuilding GTM and operating frameworks.”

“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.”

“Has experience improving an ad server workflow by standardizing genre selection with templates, enabling reuse across multiple accounts and measuring success via hours of manual labor saved. Also delivered an internal technical demo on a device-metrics-to-AWS-CloudWatch logging workflow using whiteboarding and PowerPoint to onboard a new team.”

“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.”

“Data/analytics professional with experience across manufacturing and enterprise environments (Wisconsin School of Business project with CNH Industrial; roles/projects at Ascensia Technologies, S&C, and Adobe). Has hands-on work combining warranty/lifecycle tables with technician free-text notes using TF-IDF + tree models (XGBoost/Random Forest), and deep experience in entity resolution/reconciliation across mismatched financial systems using Python/SQL and fuzzy matching, with production-grade pipeline practices in Azure Data Factory/Databricks.”

“Full-stack AI/LLM engineer who has deployed a production LLM backend (Mistral 14B) on GKE to auto-transform datasets and generate runnable ML training pipelines, addressing hallucinations, schema mismatch, latency, and burst scaling with caching/prompt compression and HPA. Also has internship experience (Splunk, BlackOffer) delivering data automation and 10+ Power BI dashboards for non-technical stakeholders with measurable efficiency gains.”

“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).”

“Full-stack engineer with Cisco supply-chain and Wipro internal platform experience, focused on customer-facing UI performance and secure backend services. Built a bulk Excel inventory upload feature (Spring Boot/Apache POI) that cut manual effort ~80%, and delivered high-scale Angular/React dashboards with strong reliability/observability (FastAPI, JWT, Docker, AWS, AppDynamics).”

“AppSec-focused customer advisor with hands-on experience integrating SAST/DAST/SCA into production CI/CD (Azure DevOps) and designing secure agent/scanning deployments in AWS (least-privilege IAM, private subnets, VPC endpoints). Demonstrates strong incident troubleshooting using logs/metrics/traces to diagnose load-related failures (timeouts/retry storms) and drive durable fixes, while tailoring risk/tradeoff communication across engineering, security, and leadership stakeholders.”