Vetted AWS Lambda Professionals

“Hands-on AI/LLM practitioner who built a RAG-based customer support chatbot and tackled production issues like data chunking complexity and response-time lag. Uses techniques such as overlapping chunks, semantic search, context engineering, and query routing, and has experience presenting technical demos/workshops to developer audiences.”

“Backend/data engineer with production experience building Python FastAPI services and AWS-native data pipelines. Has delivered containerized and serverless workloads (ECS/EKS/Lambda) with Terraform-based IaC, strong reliability patterns (JWT/RBAC, retries/circuit breakers, observability), and AWS Glue ETL into S3/Redshift. Demonstrated measurable SQL performance wins (40–50s to <4s) and owned real pipeline incidents through detection, mitigation, and prevention.”

“Backend/data engineer who built a production booking platform on FastAPI microservices (Postgres/Redis/gRPC) and delivered AWS infrastructure spanning Lambda, ECS, SQS, and Glue-to-Redshift analytics. Demonstrated measurable SQL optimization (10 minutes to <40 seconds) and strong operational ownership through monitoring, incident response, and schema-evolution hardening.”

“AI Backend Engineer in MetLife’s claims technology group who built and deployed a production LLM-based decision support system that helps claim adjusters quickly find relevant policy rules from long PDFs and historical notes. Designed it as multiple production-grade services with retrieval-first guardrails, continuous validation, and Airflow-orchestrated pipelines for ingestion, embeddings, and vector index updates to keep the system reliable as policies and data evolve.”

“Backend engineer who built and evolved a gun-parts price tracking platform focused on accurate historical pricing and fast graph-ready APIs. Experienced migrating an Express backend to NestJS incrementally with parallel routing, feature flags, and careful data integrity controls, and has a security-focused approach to API design (JWT/OAuth, RBAC, row-level access via scoped queries).”

“Backend/cloud engineer who built an AWS serverless IoT system that computes Bluetooth beacon locations from telemetry using heavy scientific Python (NumPy/SciPy/pandas) packaged as Dockerized Lambda, integrated with Java microservices and scheduled batch orchestration. Has deep AWS delivery experience (CI/CD with Code* tools, CloudFormation, cost controls) and has led high-severity incident response including CloudTrail forensics and infrastructure recovery after a compromised-keys crypto-mining attack.”

“Recent master’s graduate who led a team project building an LLM-based chatbot with RBAC-controlled information disclosure and a focus on reducing hallucinations. Also has hands-on embedded robotics experience (Arduino obstacle-avoiding robot using ultrasonic sensors) and practical DevOps/cloud deployment exposure with Docker, Terraform, Jenkins, and AWS (EKS/ECS/CodePipeline).”

“Built and shipped a production multi-agent personal financial assistant at AlphevaAI on AWS ECS, combining FastAPI microservices, Redis/SQS orchestration, and Pinecone-based hybrid RAG (semantic + BM25) to ground financial guidance. Improved routing accuracy with an embedding-based SetFit + logistic regression intent classifier feeding an LLM router, and optimized UX with live streaming plus cost controls via model tiering and caching.”

“Built a production RAG-based healthcare chatbot to retrieve patient medical documents spread across multiple platforms, reducing manual and error-prone searching. Implemented semantic search with custom embeddings (Hugging Face) and Pinecone, deployed via FastAPI/Docker on AWS SageMaker with MLflow tracking, and optimized fine-tuning cost using LoRA while orchestrating retraining pipelines in Airflow.”

“GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.”

“LLM/agent builder who shipped a live consumer AI-agent app (kalpa.chat) that visualizes complex reasoning as interactive graphs and abstracts multi-provider model usage via a unified wallet. Professionally has applied LangChain/LangGraph to IVR parsing and to scaling a football video-generation pipeline at DAZN, including shipping a VAR-specific retrieval/order fix via SQL after iterating with a non-technical PM.”

“Forward-deployed engineer who built a production AI drone-control chatbot that lets users fly a drone via natural language while viewing a real-time feed. Implemented RAG over drone SDK documentation (vector DB + top-k retrieval) and LoRA fine-tuning, with a focus on latency, token efficiency, and cost reduction, and regularly works with non-technical clients to integrate and explain AI system architecture.”

“Unity XR developer who has shipped multiple Meta Quest apps, including an NHS medical training VR simulation where realistic, consultant-validated interaction mechanics and highly configurable systems were critical. Also built and shipped a solo indie AR product ("Eugene's AR Wiki") end-to-end, supporting a wide range of iOS/Android devices and incorporating analytics, interactive content, and engagement features like word games and face filters.”

“Frontend engineer who has led customer-facing web products end-to-end, with strong emphasis on scalable component architecture, design systems, and automated quality gates (CI + unit/integration/E2E). Experienced building complex React+TypeScript dashboards with thoughtful state separation and shipping fast via feature flags/canary releases while monitoring and optimizing real-world performance issues.”

“LLM-focused engineer with end-to-end experience shipping an OpenAI-powered edtech teacher assistant into production, using Humanloop-driven prompt iteration, rigorous observability (Datadog), and A/B testing tied to real learning metrics (25% comprehension lift). Also led adoption-driving technical demos at SiriusXM (event-driven AWS Lambda/Kotlin/CDK pipeline cutting processing from 24 hours to seconds) and partnered with sales at Spresso.ai to close eCommerce SDK deals and boost activation from 40% to 85%.”

“Built and owned end-to-end ordering and inventory/order management systems for a wholesale distributor, delivering an MVP quickly and iterating based on direct observation of daily users. Experienced with TypeScript/React + Node.js layered architectures and microservices using RabbitMQ, including real-world scaling issues (duplicates, backpressure) and observability practices (correlation IDs, structured logging).”

“Built and owned UPS tracking/reporting and operations workflow dashboards, delivering customer-facing APIs and real-time React/TypeScript UIs backed by .NET Core. Experienced in high-volume microservices using IBM MQ/Azure Service Bus with strong reliability patterns (idempotency, retries, DLQ) and Azure-based observability, plus performance tuning across frontend and SQL-backed services.”

“Backend/platform engineer who built real-time connected-vehicle telemetry analytics at Subaru, spanning Kafka streaming, Python/FastAPI ETL, and low-latency WebSocket delivery (minutes to <2s). Strong Kubernetes + GitOps practitioner across AWS EKS and Azure AKS (Helm, ArgoCD, Jenkins/GitLab) and has led major on-prem-to-cloud migrations for financial microservices using Terraform and AWS DMS with measurable cost and reliability gains.”

“Built and deployed a production AI “term explainer” agent that adapts explanations to beginner/intermediate/expert users by combining multi-step LLM reasoning with grounded Wikipedia retrieval. Owns end-to-end agent orchestration (smolagents/Python), reliability patterns (fallback across LLM providers, retries, guardrails), and observability/metrics-driven evaluation; also partnered with a non-technical researcher to deliver a plain-language research assistant agent.”

“Built and deployed a production RAG-based AI assistant for sales reps to unify access to product info, pricing, and internal documents across multiple systems. Implemented ETL pipelines for normalization/chunking/embeddings, integrated the assistant into internal React/TypeScript UIs with user-specific context, and enforced security with private vector storage and permission-filtered retrieval.”

“LLM/MLOps engineer who has shipped a production RAG-based technical documentation assistant (FastAPI) cutting manual review by 45%, with deep hands-on retrieval optimization in Pinecone/LangChain (HNSW, hybrid + multi-query search, caching). Also brings healthcare domain experience—building Airflow-orchestrated EHR pipelines and delivering FDA-auditability-friendly predictive maintenance solutions using SHAP/LIME explainability surfaced in Power BI.”

“ML/LLM engineer with production experience building a compliant RAG-based virtual assistant at Intuit, optimizing embeddings and FAISS retrieval (including PCA) for low-latency, privacy-controlled search and deploying via AWS SageMaker containers. Also built scalable Airflow+MLflow pipelines using Docker and KubernetesExecutor, cutting training cycles by 37%, and partnered with civil engineers/project managers at Aegis Infra to deliver predictive maintenance for construction equipment.”

“Robotics software architect who built an automated pick-and-place palletizing prototype at BLACK-I-ROBOTICS, spanning perception (multi-RealSense fusion, segmentation, 6D pose, ICP), GPU-accelerated motion planning (MoveIt 2 + NVIDIA CuRobo), grasp generation, and safety (human detection + safe mode). Also brings cloud/CI/CD depth from VERIDIX AI (AWS Cognito/Lambda/ECS and CodePipeline stack) and demonstrated strong debugging chops by reducing outdoor rover EKF drift to ~5 cm via Allan variance-based IMU tuning.”

“Backend/infrastructure engineer with experience across API development (FastAPI/MySQL/SQLAlchemy), Kubernetes deployments, and large-scale data processing—built a Dockerized Python pipeline to pre-aggregate ~1B Graylog events for efficient querying. Has enterprise infrastructure automation background at Hewlett Packard Enterprise (Datafabric) using Terraform/Ansible with fail-fast and rollback practices, plus Kafka-based sensor streaming prototypes to Google Cloud with Java workers and autoscaling.”