Vetted AWS Lambda Professionals

“Software engineering intern with hands-on ownership of a Java/Spring Boot order management microservice, including production performance tuning via Redis caching and database indexing driven by API logs/metrics. Also contributed to a production mobile-backend LLM feature using RAG with embeddings over structured data and documents (DB + object storage), with guardrails to keep responses grounded.”

“Full-stack engineer with deep startup experience building products from scratch under ambiguous requirements. Delivered a scalable, admin-configurable notification platform (Spring Boot/Java/Kafka) supporting 50+ notification types across 3 channels for 10k+ users, cutting new notification setup to ~5 minutes. Also built a Tinder-meets-LinkedIn job-swiping app (React/TS + Node/Prisma) and has hands-on AWS production ops (ECS/EKS, RDS, CloudWatch) plus multiple third-party integrations (Stripe, QuickBooks, Twilio).”

“Backend/data engineer focused on production AWS systems: builds multi-tenant FastAPI services on ECS behind API Gateway/ALB with serverless orchestration (Lambda, SQS, Step Functions) and strong reliability practices (JWT/JWKS auth, idempotency, backoff retries, structured logging). Also delivers AWS Glue/PySpark ETL pipelines with schema/data-quality controls and has modernized legacy analytics logic into Python with parity validation; improved a key dashboard SQL query from ~12–25s to ~2–3s.”

“Backend-focused engineer who built a Python-based automation system leveraging Gemini AI and prompt-driven PDF field extraction to replace a previously manual third-party workflow. Drove stakeholder alignment around accuracy/acceptance thresholds and added production-minded safeguards like graceful failure handling and backup model contingencies.”

“Senior backend/data engineer who has built and maintained HIPAA-compliant, real-time clinical FastAPI services on AWS, orchestrating ML/LLM and vector DB calls with strong reliability patterns (auth, timeouts/retries, graceful degradation, idempotency). Also delivered AWS IaC/CI-CD (Terraform/Helm/GitHub Actions) across EKS/Lambda/SageMaker and built Glue/Spark ETL with schema evolution and data quality controls, plus demonstrated large SQL performance wins (15 min to <9 sec) and hands-on incident ownership.”

“Backend/platform engineer who built and evolved a large-scale healthcare document processing system (OCR + LLM orchestration) in Python/FastAPI on Google Cloud (Cloud Run, GCS, Firestore), processing ~1.5M files per batch and tens of millions overall. Emphasizes reliability and operational safety via deterministic IDs, idempotent state machines, strong observability, and self-healing reconciliation, plus disciplined migrations using dual-run validation and incremental rollouts.”

“Built and shipped "OnCall Copilot," a production Slack-based RAG assistant that answers on-call questions from runbooks and postmortems with citations using a FAISS vector index. Emphasizes reliability and measurable performance via strict guardrails ("no evidence, no answer"), evaluation metrics, drift monitoring, and operational hardening with Docker, logging, health checks, and offline fallback.”

“LLM/AI agent engineer with consulting/contract experience (Kanhaiya Consulting LLC) who deployed a production AI agent to automate BIM list workflows end-to-end—from database understanding and data cleaning to automated visualizations/dashboards. Worked around restricted real-time data access by generating synthetic data and improving outputs via supervised fine-tuning, and uses AWS-based LLMOps observability (Opic/OPEC) plus hybrid retrieval (vector+BM25 with reranking) to optimize relevance, latency, and cost.”

“LLM/agentic-systems practitioner who shipped a recruiting resume-assistant from prototype to production, tackling hallucinations and multi-format document ingestion (PDF/images with OCR). Strong in real-time workflow debugging (logs/traces, reproducing prod issues) and pragmatic mitigation (feature flags), and helped drive customer adoption by presenting impact data and creating educational materials.”

“Backend/data engineer who built an end-to-end real-time stock analytics platform: ingesting multi-source market data via Kafka/APIs, transforming it into dashboard metrics (e.g., Bollinger Bands), and storing in BigQuery/MySQL. Strong DevOps/GitOps experience deploying Python/Node microservices on Kubernetes with Docker/Helm, CI/CD (GitHub Actions/Jenkins), and ArgoCD, plus hands-on troubleshooting and migration work.”

“Backend engineer with experience modernizing legacy systems and building modular Python/Flask services, including a REST-to-GraphQL migration for an e-commerce platform that improved API response time by 45%. Strong in performance and scalability work across PostgreSQL/SQLAlchemy (indexing, JSONB, N+1 fixes, connection pooling) and high-throughput systems (Celery + Redis), plus integrating ML microservices with TorchServe, Kafka streaming, feature stores, and Prometheus/Grafana monitoring.”

“Backend Python engineer who owned the full backend build of an AI-driven platform for UK golf clubs, including FastAPI microservices, vector search, and a tuned LangChain+Pinecone RAG pipeline focused on cost and hallucination reduction. Experienced deploying Django/FastAPI/Flask stacks on AWS-backed Kubernetes with GitOps/ArgoCD-style delivery, plus executing legacy-to-AWS migrations and building Kafka-based real-time analytics pipelines.”

“Backend/platform engineer who has owned FastAPI microservices for analytics/ML ingestion and driven measurable performance gains (cut latency ~40%). Experienced deploying to AWS (ECS/EKS) with GitOps (GitHub Actions + ArgoCD + Helm), and has executed an on-prem to AWS migration using Terraform with parallel-run cutover and ~30% runtime improvement. Also built Kafka-based real-time user activity streaming with Prometheus/Grafana observability.”

“Full-stack engineer with strong production experience improving performance and reliability of data-heavy analytics products. Has shipped end-to-end features spanning Node/Express + PostgreSQL + Redis and React/TypeScript, deployed via Docker/GitHub Actions to AWS EKS with Helm, and monitored with Datadog/CloudWatch; also built a Python compliance automation backend for AWS security monitoring with RBAC, versioned REST APIs, and resilient throttling-aware processing.”

“Full-stack/edge-focused engineer who took a manual, terminal-based AI calibration workflow and turned it into a web-enabled remote calibration system designed for low-bandwidth 5G field deployments, now used across 85+ field sites. Experienced operating edge fleets with versioned rollouts, Kubernetes-based cloud monitoring, and Prometheus/Grafana observability, plus refactoring fast-moving AI codebases for modularity and strong typing.”

“Full-stack engineer who shipped and owned a production Document Chat feature built with Next.js App Router/TypeScript and a Node/Express RAG backend, including JWT-secured route handlers and streaming responses. Demonstrated strong post-launch ownership by improving latency (~30%) via MongoDB indexing/query optimization and reducing AI costs through caching, backed by profiling with React Profiler and Chrome DevTools.”

“Founder building a hiring-focused startup who has engaged with venture investors but is prioritizing direct end-user traction through email marketing and other outreach before returning to raise a seed round. Has experience working with startups that have already raised seed funding and demonstrates a structured approach to market validation (customer conversations, landing pages) prior to heavy development.”

“Software engineer (internship experience) who built and owned an AWS serverless multi-user “challenge” feature end-to-end (UI + REST APIs + DynamoDB + deployment), delivering measurable gains in latency (-30%), debugging time (-50%), and join drop-offs (~-30%). Also productionized a multilingual RAG-based QA system with vector retrieval and guardrails, improving accuracy to ~85% and driving ~20% DAU growth.”

“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).”

“Backend engineer who has led redesigns and migrations for a real-time logistics platform, improving scalability and resilience while managing eventual consistency tradeoffs. Demonstrates strong distributed-systems rigor (idempotency, transactions, async queues, monitoring) and builds secure, versioned FastAPI APIs with JWT/OAuth2, RBAC, and database row-level security.”

“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.”

“Built and deployed a production AI career-advice agent designed to combat unreliable/generic LLM guidance by grounding outputs in retrieval-first RAG over resumes/job/hiring data, with multi-step reasoning, structured memory, and evidence-only prompting to reduce hallucinations. Implemented the system with LangChain/Python and deployed on AWS as scalable microservices orchestrated via REST and asynchronous calls, iterating closely with career coaches and students.”