Vetted AWS Lambda Professionals

“Full-stack/ML-leaning engineer who has shipped production-grade real-time analytics and an internal AI support assistant using RAG over enterprise documentation. Demonstrates strong systems thinking across scalability, reliability, observability, and LLM safety/evaluation (thresholded retrieval, RBAC, response validation, regression-gated evals), with concrete iteration based on performance metrics and user feedback.”

“Data engineer who has owned production pipelines end-to-end—from Kafka/Airflow ingestion through SQL/Python validation and dbt transformations into Redshift/BI. Also built and operated a large-scale distributed web scraping platform (50–100 sites daily, ~5–10M records/day) with Kubernetes, Kafka queues, robust retries/DLQ, anti-bot measures, and backfill-safe raw HTML storage.”

Hammad Ur Rahman

Screened

Senior DevOps Engineer specializing in cloud infrastructure and CI/CD

Boston, Massachusetts11y exp

“IBM Power/AIX engineer who has owned a 150+ LPAR AIX 7.x estate with VIOS/HMC/vHMC and PowerHA in production, including real outage and failover recoveries. Also brings modern DevOps/IaC experience—built Jenkins pipelines deploying to AKS and implemented Terraform on AWS with remote state, locking, and drift management.”

Sharanya Rao

Screened

Mid-level Backend & Blockchain Engineer specializing in Cosmos SDK and EVM

Remote, USA4y exp

“Built and productionized an LLM+RAG lending assistant on AWS to help loan officers quickly answer questions from credit policies and prior decisions, tackling hallucinations with retrieval-only responses and a no-context fallback. Also automated end-to-end ETL and model retraining/deployment using Apache Airflow, and has experience translating clinical stakeholder needs (doctors/care managers) into ML features, metrics, and dashboards.”

Satish Kumar Reddy

Screened

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

Remote, NJ5y exp

“Built and deployed a production LLM/RAG intelligent document understanding platform for healthcare clinical documents (notes, discharge summaries, diagnostic reports), integrating spaCy entity extraction, Pinecone vector search, and a Spring Boot API on AWS with monitoring and guardrails. Demonstrates strong MLOps/orchestration (LangChain, Airflow, Kubeflow/Kubernetes) and a metrics-driven evaluation approach, and partnered with a healthcare operations manager to cut manual review time by 80%.”

JUNG NO

Screened

Senior Solutions Engineer & Applied AI Builder specializing in agentic workflows

Remote, US9y exp

“Built and shipped a production AI booking/quoting system for a Spanish-speaking cleaning business serving English-speaking customers, covering the full booking and payment flow and generating bilingual SEO/AEO content. Uses Gemini/Genkit with multi-agent orchestration (ADK/MCP, LangChain) and a production stack on Vertex AI + Cloud Run + Terraform, with analytics wired from Google Analytics to BigQuery for measurable agent performance.”

Shiva Dasi

Screened

Mid-level Software Engineer specializing in LLM agents and cloud-native systems

Boston, MA4y exp

“Built and shipped production LLM agents in compliance-sensitive environments (FERPA), emphasizing reliability via structured outputs, state-graph orchestration (LangGraph), and CI-driven eval/regression testing. Also has experience hardening messy ERP ingestion pipelines at scale (50K monthly orders) with normalization, idempotency/deduplication, and robust failure handling using AWS (SQS/CloudWatch) and PostgreSQL.”

Jyothsna V

Screened

Mid-level Backend Software Engineer specializing in Python/FastAPI and cloud-native microservices

USA4y exp

“Backend engineer who evolved Coca-Cola bottlers' Trade Promotion Optimization platform at Coke One North America, building domain-focused microservices in Node.js and Python (Flask/FastAPI) with PostgreSQL. Experienced in multi-tenant security (OAuth2/JWT, RBAC, row-level scoping by bottler/region), API contract/versioning discipline, and Azure DevOps-driven incremental rollouts with strong observability.”

Mrudula Devaguptapu

Screened

Mid-Level Software/AI Engineer specializing in backend systems, data pipelines, and RAG automation

United States3y exp

“Backend engineer with experience modernizing high-traffic subscription and payment systems (TCS) by moving to event-driven Spring Boot microservices with Kafka, adding idempotency/state management to eliminate duplicate processing. Built and scaled FastAPI services for AI automation workflows (360DMMC) with versioned contracts, JWT security, and strong observability, and has led live refactors using feature flags, parallel runs, and data reconciliation.”

Ashay Panchal

Screened

Mid-level Software Engineer specializing in AI-driven distributed systems

San Jose, CA4y exp

“Backend engineer who built a high-stakes, privacy-first platform at be Still Analytics for survivors of domestic violence, emphasizing anonymity, security, and reliability. Experienced with GenAI backends (LangChain + AWS Bedrock) including RAG to prevent hallucinations, plus cloud-native scaling (Docker/Kubernetes) and cost-saving migrations from legacy VMs to serverless (30% reduction).”

Berkan Özgür

Screened

Senior XR Engineer specializing in spatial computing and cross-platform Unity systems

San Jose, CA9y exp

“XR/Unity developer who built an underwater experience (Visutate) featuring reactive flocking fish that respond to user presence and hand-feeding interactions. Also implemented multiplayer using Unity Netcode, addressing sync challenges via reduced streamed data and client-side prediction; prefers impactful work in startup-style environments.”

Kundhana Paruchuru

Screened

Mid-level Data Scientist specializing in ML, LLM pipelines, and MLOps

Remote, USA3y exp

“Built and deployed a production LLM-driven document understanding pipeline using LangChain/LangGraph, focusing on reliability via step-by-step prompting, validation checks, and monitoring. Also partnered with non-technical marketing stakeholders at Heartland Community Network to deliver an XGBoost targeting model surfaced in Power BI, improving campaign conversion by 12%.”

Yash Amre

Screened

Intern Data Scientist specializing in machine learning and NLP

California, USA1y exp

“Analytics-focused early-career candidate with internship experience owning reporting and system performance analysis projects end to end. They combine SQL data preparation, Python automation, and dashboard delivery with measurable impact, including roughly 50% less manual reporting and about 20% better forecast accuracy.”

“Full-stack product engineer focused on data-heavy dashboard applications, with hands-on ownership from React/TypeScript UI through Node/Express APIs to Postgres schema design and optimization. Stands out for combining product sense with engineering rigor: improving onboarding and reporting flows using analytics and user feedback, while also building reusable upload infrastructure and safe multi-tenant configurable experiences.”

“Full-stack product engineer with strong web3/blockchain experience, having built invoicing/tokenization, transaction explorer, and no-code workflow products across React, Node.js/serverless, and SQL. Stands out for combining deep technical execution with product thinking—improving onboarding completion by ~40%, shipping quickly under ambiguity, and creating reusable platform primitives for auditability and multi-tenant customization.”

“Backend-focused engineer with experience at Ramp, Easebuzz, and George Mason University, spanning data pipelines, workflow automation, and production reliability. Stands out for quantifiable performance gains, strong debugging instincts in distributed job systems, and translating ambiguous finance operations processes into measurable automation outcomes.”

Keeravani Chekuri

Screened

Mid-level AI/ML Engineer specializing in LLM systems and MLOps

Boston, MA3y exp

“Built and deployed an AI tutoring assistant end-to-end at Nexora School, spanning discovery with school districts, multi-agent LangGraph/RAG architecture, AWS Bedrock migration, and post-launch stabilization. Stands out for combining hands-on LLM systems engineering with strong educator-facing trust building, FERPA-driven architecture decisions, and disciplined production practices around evals, logging, and messy document ingestion.”

“Built and deployed a full-stack web product for international football fans visiting the U.S. for FIFA, owning everything from crawling and aggregating event data to frontend, backend, deployment, and maintenance. Particularly strong in data-heavy product work, using LLMs, Google Maps API, and SQL/RPC patterns to improve data quality, speed implementation, and support a polished user experience.”

Abhishek Ghaisas

Screened

Intern-level Data Scientist specializing in AI and full-stack applications

Pune, India1y exp

“Engineer with hands-on experience building production ML and Python backend systems, including a real-time social media monitoring pipeline handling 1000+ events per second and a prototype AI operations assistant for Seattle-Tacoma Airport. Stands out for combining reliability engineering, automation, and LLM/NLP-to-SQL work, with measurable impact such as improving uptime from 92% to 99.4%.”

“Healthcare software sales candidate with hands-on experience building outbound campaigns, refining ICP and messaging in a small, iterative environment, and selling AI-enabled EMR capabilities. Demonstrates strong consultative discovery in nuanced care settings like home health and home care, with measurable outbound results including a 12% reply rate, 5-10 demos, and 25% demo conversion.”

“Engineer with hands-on experience modernizing healthcare platform authorization and EVV compliance workflows, including replacing hardcoded permissions with a Cerbos-based RBAC/ABAC system. Stands out for pragmatic AI-assisted development in regulated environments, with a strong emphasis on testing, auditability, and catching subtle business-rule failures before production.”

Atharva Jagtap

Screened

Junior Full-Stack Software Engineer specializing in cloud and AI/ML applications

United States, Remote2y exp

“Full-stack engineer with hands-on experience across e-commerce personalization, enterprise RAG assistants, and cloud infrastructure automation. They’ve shipped AI features using Azure LLM APIs and vector search, improved recommendation engagement, and worked across frontend, backend, ML-informed analytics, and AWS infrastructure in early-stage environments.”