Vetted AWS Lambda Professionals

“Full-stack and AI systems engineer who has shipped both enterprise fintech and real-time voice AI products. At NCR, they repaired a broken transaction-fee workflow end to end using React/TypeScript, Lambda, and DynamoDB; at Lillup, they turned a research voice prototype into production infrastructure, cutting latency from 350ms to under 200ms and raising user ratings from 2.8 to 4.1. They also built an LLM side project that uses Claude and GPT to cross-review each other through structured JSON consensus rounds.”

“Full-stack engineer with strong DevOps/AWS experience who ships end-to-end React/TypeScript + Node/Python systems and operates them in production. Built an LLM-assisted recommendations workflow for a SaaS product with robust reliability controls (schema-validated JSON outputs, fallbacks, caching, monitoring) and measured impact via adoption, time saved, and override rates; also experienced delivering MVPs fast in early-stage startup ambiguity.”

“Backend engineer experienced in evolving high-scale legacy on-prem systems into cloud-native, event-driven microservices on AWS/Kubernetes (noted peak traffic ~1.5M QPS). Strong focus on reliability engineering and operational excellence—SLO-driven observability, GitOps/canary rollouts, chaos testing, and preventing cascading failures (e.g., retry-storm mitigation).”

“Backend/data engineer with hands-on production experience across Azure and AWS: built FastAPI + PostgreSQL services with Azure AD OAuth2/JWT auth and strong reliability patterns (timeouts, retries, correlation IDs). Delivered AWS Lambda/ECS solutions with Terraform/CI-CD and cost controls (SQS buffering, reserved concurrency), and built/operated AWS Glue ETL pipelines into Redshift while modernizing legacy SAS reporting into Python microservices with parity testing.”

“Built and deployed production NLP/LLM systems including a multilingual (5-language) health misinformation detection pipeline with latency optimization (batching/quantization/caching) and explainability (gradient-based attention visualizations). Experienced orchestrating end-to-end AI workflows with Airflow and Prefect, and partnering with customer support ops to deliver an AI agent for ticket summarization and priority classification with clear, measurable acceptance criteria.”

“Full-stack engineer with recent enterprise experience building Spring Boot/Spring Cloud microservices on AWS (Lambda, S3, DynamoDB) and a React/TypeScript frontend. Has hands-on experience solving microservice communication timeouts via API Gateway/load balancing and implementing centralized JWT-based security, plus performance work for large data workloads using indexing, caching, and async processing.”

“Full-stack engineer currently building an employee management system end-to-end with React, Node/Express, and PostgreSQL, including JWT auth and RBAC. Previously worked at TCS on large-scale State Bank of India web applications, applying Redis caching, server-side pagination/filtering, and async job offloading to improve performance and reliability.”

“Backend engineer with fintech/transaction-processing experience who built and optimized a Spring Boot + PostgreSQL + AWS service handling money transactions, resolving peak-traffic latency via query/index and connection pool tuning. Shipped an LLM-driven risk-flagging workflow integrated via a FastAPI Python service, owning prompt design, validation guardrails, monitoring, and human-in-the-loop escalation to reduce false positives and improve precision over time.”

“Backend/infra-focused engineer who owned production systems for distributed ML experimentation (hyperparameter tuning across a cluster with GPU scaling, custom scheduling, and checkpoint-based fault tolerance). Also built and operated a low-latency log validation service using queued async workflows with idempotency, retries/backoff, and strong observability, plus experience building resilient Selenium-based browser automations for complex multi-step web flows.”

“JavaScript engineer with open-source experience on a database visualization library, focused on real-time rendering performance for large datasets (virtualized DOM rendering, requestAnimationFrame/debouncing, memoization) and on raising project quality via tests and CI performance benchmarks. Also built Kafka-based messaging documentation and sample producer/consumer apps to speed onboarding, and has experience diagnosing production issues including concurrency-related duplicate data problems.”

Krishna K

Screened

Junior Machine Learning Engineer specializing in multimodal systems and LLMs

Jersey City, NJ2y exp

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”

Lakshmi Meghana

Screened

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

Bristol, PA4y exp

“Built and deployed a transformer-based clinical document classification system that processes unstructured clinical notes in a HIPAA-compliant healthcare setting, served via FastAPI on AWS and integrated into an Airflow/S3 pipeline. Demonstrates strong end-to-end MLOps skills (data quality remediation, low-latency inference optimization, monitoring with MLflow/CloudWatch) and effective collaboration with clinicians to drive adoption.”

Prabhdeep Gandhi

Screened

Mid-level Software Engineer specializing in real-time IoT and event-driven platforms

5y exp

“Founding engineer at a startup building LLM/agentic workflows for public-safety customers, with hands-on experience delivering a hybrid on-prem + secure cloud solution to meet strict compliance needs. Implemented OpenTelemetry observability for multimodal agentic systems behind closed networks and used the resulting traces to optimize prompting/token usage for customer-specific security integrations. Regularly runs technical workshops and supports pre/post-sales by translating integration feedback into product roadmap decisions.”

Tao Chen

Screened

Senior Full-Stack Engineer specializing in AI-powered web products

Mountain View, CA7y exp

“Backend/data engineer who has built production AI video generation services on AWS using a hybrid serverless + container architecture (FastAPI, Lambda, ECS, Postgres/Redis) with strong reliability practices (auth, retries/timeouts, structured logging, CloudWatch + Slack alerting). Also delivered AWS Glue ETL pipelines with schema evolution handling and modernized a legacy SAS healthcare reporting workflow to Python with parity validation and parallel-run migration.”

Aneri Patel

Screened

Junior Machine Learning Engineer specializing in LLM fine-tuning and semantic retrieval

Washington, D.C.2y exp

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

Tanisha Dalwadi

Screened

Mid-Level Full-Stack Product Engineer specializing in TypeScript and React

Arizona, USA4y exp

“Software engineer and co-founder with 0-to-1 SaaS experience who built and owned an end-to-end reporting/analytics dashboard on Next.js App Router + TypeScript, including Postgres schema design, aggregation query optimization, and post-launch performance/monitoring. Has delivered measurable React dashboard performance gains (~35% improvement in time-to-insight) and built durable, idempotent job/state-machine workflows using serverless functions and Postgres.”

Anita Bhagashetti

Screened

Mid-Level Software Engineer specializing in distributed systems and cloud microservices

3y exp

“Built and productionized a RAG-based semantic search system for video-derived data, focusing on measurable success metrics (p95 latency, reliability, cost/request) and strong observability (prompt versions, retrieved docs, tool calls, token usage). Experienced in diagnosing real-time issues in LLM/agentic workflows and in supporting go-to-market efforts through tailored technical demos, rapid POCs, and post-close onboarding.”

Pavan Kalyan Reddy Gunamareddy

Screened

Mid-level Full-Stack Java Developer specializing in FinTech and Healthcare

TX, USA4y exp

“Backend/platform engineer in fintech/payments (NexaBank/NextBank/Nexon Bank) who has built Kafka-orchestrated Java/Spring Boot microservices around a PostgreSQL double-entry ledger. Led production-critical reliability work preventing duplicate payment postings via idempotency and offset sequencing fixes, and shipped real-time ML fraud scoring (Python model API + Redis caching) with rigorous evaluation/monitoring (Prometheus) and workflow automation for dispute resolution.”

Jayanth Amol Ergola

Screened

Mid-Level Software Engineer specializing in full-stack development and data engineering

Milpitas, California4y exp

“Backend engineer with production experience at KeyBank building high-volume Java/Spring Boot services on Azure with PostgreSQL/Oracle, including async job ingestion and tracking. Demonstrates strong reliability/performance debugging (HikariCP pool exhaustion, DB contention) and has shipped an LLM-powered data analysis/summarization feature with robust production guardrails (validation, shadow testing, deterministic fallbacks, audit logs).”

“Data engineer who has owned high-volume production pipelines end-to-end (200–300 GB/day) on AWS, implementing strong data quality/observability and achieving 99.9% reliability while cutting data issues ~33%. Also built a large-scale external data collection system ingesting millions of records/day with anti-bot/rate-limit handling and backfill tooling, and shipped a versioned REST service exposing curated Snowflake data to downstream teams.”

“Built and shipped a personal LLM-powered news aggregation platform (Clear Brief) that scrapes ~200 articles per cycle, clusters them into ~15–30 consolidated stories, and supports on-demand deep dives via a Next.js API route. Emphasizes production-minded reliability (token/cost controls, timeouts, graceful frontend degradation) and database-backed orchestration using SQLite with retry + exponential backoff for burst processing.”

Yanet Fernandez

Screened

Junior Data Analyst specializing in healthcare analytics

Fort Myers, FL2y exp

“Analytics/data professional with hands-on experience turning messy semi-structured CRM JSON data in Snowflake into clean reporting layers using SQL and validation logic. Brings a practical mix of data engineering, Python automation, metric design, and stakeholder alignment to improve reporting accuracy and speed of decision-making.”

RAUNAQ BEDI

Screened

Entry-level Software Engineer specializing in AI, data engineering, and cloud DevOps

San Francisco, CA1y exp

“Product-minded full-stack engineer with strong React/TypeScript, serverless AWS, and Postgres depth, highlighted by owning real-time personalization and onboarding experiences at mParticle. Stands out for combining deep performance debugging with measurable product impact—improving activation by 28%, reducing time-to-insights by 35%, and building reusable internal platform primitives adopted by 12 teams.”

Cristobal Troncoso

Screened

Senior Unity Developer specializing in VR, multiplayer, and AI-driven simulations

Santiago, Chile12y exp

“Unity/VR engineer who has owned systems from gameplay architecture through hardware integration, including a modular casino platform deployed to 100+ terminals in Chile that became the company’s primary revenue source for over three years. Also brings hands-on experience with AI-driven talking avatars, multiplayer VR training on Quest, and aggressive cross-platform optimization across WebGL, Android, and VR.”