Vetted Amazon CloudWatch Professionals

Narayan Anantha Krishnan

Screened

Junior AI/ML Engineer specializing in LLMs, RAG, and cybersecurity

Syracuse, NY2y exp

“AI/full-stack builder with hands-on experience shipping conversational and agentic products, including a travel itinerary assistant, a multi-agent data analysis platform, and a self-correcting RAG system. Also brings academic research depth from Syracuse University, where they helped develop tiny-LLM-based IoT threat mitigation and presented an accepted paper at FLAIRS 39.”

“New grad software engineer who has already built both a full-stack location-based social app and an internal AI on-call copilot using OpenAI and LangChain. Stands out for combining end-to-end product execution with practical LLM engineering, including RAG, fallback design, citations, and production evals, plus shipping a hackathon-winning MVP in 24 hours.”

“Built AI-powered products across both healthcare and financial services, including a privacy-conscious assistant for elderly health check-ins and a production RAG system for high-stakes financial document analysis. Stands out for combining full-stack engineering with strong LLM reliability practices—grounding, structured outputs, fallback handling, monitoring, and human-in-the-loop controls—while delivering measurable impact on accuracy, speed, and system performance.”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Backend engineer (Encore) who led the evaluation and redesign of a high-volume, low-latency real-time retrieval/ranking and inference platform on AWS, shifting from tightly coupled services to a modular architecture for better fault isolation and independent scaling. Strong focus on production reliability, observability, and security (JWT/RBAC, multi-tenant scoping, Postgres/Supabase RLS), with disciplined migration playbooks (feature flags, shadow traffic, dual writes/reconciliation).”

“Built and owned a customer-facing Document Intelligence Service for legal contract analytics at Noasis Digital, delivering extraction/summarization with careful accuracy controls (confidence thresholds, versioned deployments, production logging). Also developed a React/TypeScript document review app and internal QA dashboard, and has hands-on microservices experience with async messaging (RabbitMQ), timeout tuning, and centralized structured logging for reliability at scale.”

“Full-stack engineer with hands-on experience building end-to-end applications using Java/Spring Boot and React, including Dockerized deployment and RabbitMQ-based messaging. Worked on a high-volume payment processing system at Alacriti, focusing on performance (query optimization, caching) and reliability with monitoring via AWS CloudWatch.”

Rizwana Shaik

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native apps and AI copilots

Dallas, TX4y exp

“Internship project building and deploying a LLaMA-based, RAG-enabled copilot inside a Professional Services Automation platform, enabling natural-language navigation, text-to-SQL reporting, and project/resource/budget insights across multiple modules. Addressed real production issues like context drift and vague queries with hybrid search, metadata enrichment, and an intent classification/rewriting layer, orchestrated via Apache Airflow—ultimately cutting PMO reporting time by 40%.”

Ramya Jonnala

Screened

Principal/GenAI Engineer specializing in LLMs, RAG, and MLOps

Plano, TX9y exp

“Built a production AI-powered university marking system that automates question generation and grading from PDF course materials using a RAG pipeline (S3 + Pinecone) orchestrated with LangChain/LangGraph and deployed on AWS ECS via Docker/ECR and GitHub Actions CI/CD. Addressed a key real-world LLM challenge—grading consistency—by implementing rubric-based scoring, retrieval re-ranking, and standardized context summarization, validated against human instructors.”

Asad Abdul

Screened

Mid-level Full-Stack Developer specializing in AI-driven cloud-native applications

Laguna Niguel, CA4y exp

“Full-stack engineer with healthcare/ops analytics experience at PatientXpress, shipping a real-time operational dashboard end-to-end (React/TypeScript + Node/Postgres on AWS) that cut manual reporting by 50%. Strong in performance and reliability work—pagination/caching, Postgres indexing/partitioning, Terraform-based AWS provisioning, CI/CD with GitHub Actions, and production incident response with improved monitoring (CloudWatch/Prometheus).”

Tim Kwok

Screened

Senior DevOps Engineer specializing in multi-region AWS/GCP cloud infrastructure

United States8y exp

“Backend/data engineer with strong AWS production experience spanning FastAPI microservices and large-scale data pipelines. Has delivered containerized Python services on EKS with Terraform/Helm/GitHub Actions, implemented robust auth/secrets practices, and owned ETL reliability (Glue/S3/Redshift) including incident response and idempotent reruns. Demonstrated SQL tuning on 50M-record ETL workloads to remove SLA misses and improve reliability.”

Charmi Dharmendrakumar Parmar

Screened

Mid-Level Software Engineer specializing in full-stack and cloud-native systems

Alpharetta, GA4y exp

“Backend/full-stack engineer who owned a cloud-native, AWS-based microservices backend for an HRIS product used by ~10,000 users, including onboarding and workflow orchestration. Strong production focus on event-driven architecture, idempotency/retries, observability, and developer-friendly API design (OpenAPI, versioning, JWT), plus hands-on Selenium automation for resilient checkout-style flows.”

“AI/ML engineer with 9+ years of experience building production recommendation and LLM systems end-to-end, from experimentation through deployment, monitoring, and retraining. Stands out for combining strong MLOps discipline with practical GenAI/RAG implementation, including measurable impact such as ~25% higher engagement on an e-commerce recommender and nearly 30% faster knowledge retrieval from internal documents.”

“Full-stack engineer who built WordCon, an AI-powered vocabulary learning platform, end-to-end across React/Next.js, Python, AWS, and GenAI services. Particularly strong at turning ambiguous AI product ideas into structured, scalable systems by combining deterministic learning logic with LLM-powered personalization, and has additional experience modernizing legacy PHP systems into React/Node architectures.”

“Frontend-focused product builder who designed and implemented an internal PCB quoting and pricing platform at SVTronics end to end. They translated a vague operational problem into a shipped React/TypeScript workflow that cut quote preparation time by about 40% and reduced manual errors, showing strength in both UX simplification and production delivery.”

“Full-stack engineer with strong AWS operations experience who helped replace a long-standing manual logistics reporting process by building a production-grade, event-driven On-Time-Performance rules system. Personally owned the Vue-based rule configuration frontend end-to-end (design collaboration through QA/UAT and post-release support) and measures success via accuracy validation against historical data, reduced manual adjustments/tickets, and system latency/error metrics.”

Emma Choudhary

Screened

Senior Full-Stack Software Engineer specializing in Python, Django, and Generative AI

United States6y exp

“Backend/data engineer with hands-on production experience building partner-facing Python APIs (FastAPI, Celery, Postgres/Redis) and AWS serverless data platforms (Lambda, SQS, Step Functions, Glue). Emphasizes reliability and governance—JWT tenant-scoped auth, secrets/config hygiene, data-quality quarantine, and incident ownership—plus measurable SQL tuning that eliminated timeouts and stabilized reporting workloads.”

“Cloud infrastructure/DevOps engineer focused on AWS/Azure production environments, with hands-on experience running EKS-based platforms, Terraform-driven IaC, and secure Jenkins/GitLab CI pipelines. Has led real-world migrations from EC2 to Kubernetes using blue/green cutovers and executed multi-AZ failover testing with documented same-day recovery and no data loss; does not have direct IBM Power/AIX ownership experience.”

“Infrastructure engineer focused on AWS platform automation: built a full CloudFormation-based environment (networking, Aurora, ElastiCache) and created Python tooling plus Lambda-backed custom resources to automate deployments, user provisioning (MySQL/RabbitMQ), and operational workflows. Also migrated a company from Rackspace to AWS and maintains an 8-year Debian homelab with VMs/containers, RAID6 backups, and GPU passthrough troubleshooting. No Kubernetes experience.”

“Early-career automation/workflow builder who has owned small-scale end-to-end implementations (discovery through stabilization), using tools like Zapier, Google Sheets, and Airtable with strong validation and troubleshooting habits. Built and tested a production-style RAG application on AWS Bedrock (S3 + Knowledge Bases + embeddings + OpenSearch + retrieve-and-generate) with a Streamlit UI to deliver more grounded, context-aware document Q&A.”