Vetted Amazon CloudWatch Professionals

“Currently at Huntington Bank, built a production-grade RAG system that helps business/operations teams get grounded answers from large volumes of internal enterprise documents. Owns ingestion and FastAPI backend, tuned hybrid BM25+vector retrieval and chunking for relevance, and evaluates reliability with metrics and observability (LangSmith, CloudWatch, Prometheus/Grafana) while partnering closely with non-technical stakeholders.”

“QA automation engineer who owned and transformed a JavaScript/WebdriverIO UI automation suite into a reliable CI release gate by refactoring to POM, adding API validations, cross-browser coverage, and aggressively reducing flakiness. Demonstrated strong security mindset by uncovering a critical RBAC gap where restricted users could trigger billing actions via direct API calls, and helped reshape product requirements early through detailed acceptance-criteria questions (e.g., CSV upload partial failures).”

“Software engineer with payments-domain experience (Visa) building real-time transaction monitoring and analytics systems. Strong end-to-end ownership across Spring Boot/Kafka microservices, PostgreSQL modeling, and AWS/Kubernetes operations, plus React+TypeScript dashboards—focused on low-latency processing, secure APIs, and zero-downtime production releases.”

“Full-stack engineer with fintech trading-platform experience who shipped and operated a real-time portfolio P&L/performance feature end-to-end (React + Node/WebSockets + MongoDB) on AWS, including significant performance tuning under peak trading load. Also built a Spark-based trading analytics pipeline with idempotency and reconciliation for auditability, and has a personal React/TS + Node/Express project (Artsy) with JWT auth and schema-evolution practices.”

“Data engineer focused on high-volume transaction pipelines (2M+ per day) using Snowflake/Snowpipe, Spark/PySpark, Kafka, and Airflow, with a strong emphasis on schema/data-quality enforcement and reliability improvements. Also built a greenfield compliance-focused RAG solution, using CloudWatch monitoring and adding ingestion validation to prevent malformed OCR documents from degrading search quality.”

“Cloud/platform engineer with hands-on ownership of AWS EKS Kubernetes platforms built and upgraded via Terraform, including AWS networking/security, EBS/EFS/S3 storage integration, and reliability validation through CloudWatch plus Prometheus/Grafana. Also has on-prem VMware/vSphere administration experience and day-to-day hybrid on-prem-to-AWS operations (VPN/Direct Connect), with examples of resolving pod instability from an application memory leak and fixing a production connectivity drop via routing/firewall troubleshooting.”

“Grad student who co-developed a safety-oriented mental health LLM consulting agent using RAG + Gemini and Hugging Face emotion detection to assess user crisis level and adapt responses. Implemented a key reliability improvement for CRISIS scenarios by bypassing generative output and returning direct, emotionless, knowledge-base guidance to seek immediate real-world help.”

“Data engineer in healthcare (HCA) who owned end-to-end Azure-based pipelines at very large scale (50M+ daily claims/patient records). Strong focus on reliability: schema-drift fail-fast validation, quarantine layers, and Python/SQL data quality checks that reduced issues ~25%, plus performance tuning in Databricks/PySpark and versioned serving in Synapse for downstream consumers.”

Nikitha Kommidi

Screened

Mid-level AI/ML Engineer specializing in fraud detection, NLP, and MLOps

6y exp

“Built a production real-time fraud detection and customer-support automation platform at Citibank, tackling extreme class imbalance (reported ~1:5000) and strict latency constraints. Combines hands-on MLOps (Airflow, Kubernetes, MLflow; Snowflake/Spark/S3 integrations; CI/CD model promotion) with cross-functional delivery to Risk & Compliance focused on interpretability and reducing false positives.”

Sravanti Dandu

Screened

Mid-level Cloud Engineer specializing in AWS & Azure infrastructure automation

Arizona, USA6y exp

“Backend/platform engineer (American Express) who built a Flask-based orchestration layer to automate infrastructure provisioning and integrated Azure AD/JWT RBAC security. Strong in PostgreSQL/SQLAlchemy performance optimization (70%+ query-time reduction) and scalable async/event-driven architectures, including ML inference pipelines (SageMaker/Azure ML/Hugging Face) and high-throughput job queues (Celery/Redis) with reliability patterns like DLQs and idempotency.”

Gnaneswar Markat

Screened

Mid-level Software Engineer specializing in cloud-native distributed systems

Charlotte, NC4y exp

“Full-stack engineer with Bank of America experience building and owning a customer portfolio dashboard end-to-end, from requirements through launch and ongoing iteration. They combine React/Spring Boot/PostgreSQL implementation with strong performance tuning, real-time data handling, and UX improvements, and cite adoption by roughly 12,000 active internal users.”

Pavan Punna

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and healthcare-fintech AI

Dallas, TX5y exp

“Built and owned a production GPT-4 RAG assistant for clinical and enterprise query resolution, taking it from initial experiment to deployment, monitoring, and iterative improvement. Their work cut resolution time from 45 minutes to under 2 minutes, achieved roughly 95% accuracy, and scaled to thousands of additional monthly queries while emphasizing safety and trust in a sensitive clinical domain.”

“ML/AI engineer with production experience at S&P Global and Accenture, focused on regulated, enterprise-grade systems. Built end-to-end financial risk and credit default models with >90% precision and 12% fewer false positives, and is currently developing secure RAG pipelines on AWS SageMaker for enterprise insight extraction.”

premkumar narla

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG, and enterprise ML systems

Chicago, IL5y exp

“ML/AI engineer with hands-on experience at Morgan Stanley building production fraud detection and enterprise RAG systems. Stands out for owning systems end-to-end—from experimentation and deployment to monitoring and iteration—and for delivering measurable impact, including an 18% reduction in fraud false positives, 40% lower inference latency, and internal tooling that reduced model deployment time from days to hours.”

“Built Nexthire-AI, shipping an end-to-end LLM-powered resume–job description matching product (React + Node.js) using embeddings and retrieval to generate match scores and skill-gap recommendations. Improved post-launch engagement by making feedback cleaner and more actionable, and added production guardrails (validation, timeouts, fallbacks) to handle messy resume formats and AI API instability.”

“Engineer with a process-driven approach to AI-assisted software development, focused on orchestrating where AI adds value while maintaining human review and verification. Has applied this in backend work such as an S3-based invoice pipeline and used multi-agent workflows to speed up large API refactors across many endpoints.”

“Backend-oriented software engineer who uses AI as a disciplined engineering accelerator rather than a replacement for judgment. They have hands-on experience coordinating multiple AI tools/agents across code generation, testing, debugging, and review, with strong emphasis on security, scalability, and human validation before production use.”

Aditya Rao

Screened

Mid-level Software Engineer specializing in backend, AI, and distributed systems

San Jose, CA5y exp

“Software engineer with 4.5 years of startup experience across programmatic advertising, health tech e-commerce, and automobile diagnostics, plus both bachelor's and master's degrees in CSE. Built an agentic global supply chain platform in a hackathon using a highly structured AI-first workflow, and has hands-on experience designing multi-agent debate systems, rollout safeguards, and observability-driven production fixes.”

“Senior software developer with hands-on experience building real-time React + TypeScript dashboards and map-based visualization tools for large datasets. Stands out for practical frontend performance work in production, including memoization, virtualization, debouncing, lazy loading, and Google Maps optimization for thousands of records or points.”

“Built internal product features at Sysco's Collab Cafe across React/TypeScript frontend and Spring Boot/PostgreSQL backend, including a full project invite flow and an early AI-style project matching capability. Stands out for owning features end-to-end, improving React dashboard performance with profiling and component refactoring, and making pragmatic 0→1 tradeoffs to ship quickly.”

“Banking-focused full-stack engineer who has owned products from requirements through deployment, including a transaction review dashboard and an AI-assisted search tool for internal support teams. Brings a strong mix of React/TypeScript, Spring Boot, database performance tuning, and practical LLM/RAG experience with human-in-the-loop safeguards for regulated workflows.”

Katha Naik

Screened

Intern software engineer specializing in AI, cloud, and full-stack systems

Fremont, CA2y exp

“Engineer with experience at Fox Corporation and Qualcomm, focused on production automation and AI-powered systems. At Fox, they built a serverless Bedrock Operations CoPilot for broadcast/media operations that centralized fragmented operational data and cut incident investigation time by 50-60% across distributed teams and stations. They also bring applied LLM experience from Qualcomm, where they worked on a safer RAG-based learning assistant for children with autism spectrum disorder.”

Pallavi Thorat

Screened

Mid Software Engineer specializing in backend, cloud, and AI systems

USA, USA3y exp

“Front-end engineer with strong experience building sophisticated, data-heavy financial dashboards for finance analysts and business stakeholders. Stands out for combining React/Next.js architecture, D3.js visualization, and browser-level performance tuning to deliver measurable impact, including ~40% operational efficiency improvement and ~35% lower session drop-off.”