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Vetted Amazon Bedrock Professionals
Pre-screened and vetted.
Junior AI/ML Engineer specializing in Generative AI production systems
Junior Full-Stack AI Developer specializing in multi-agent LLM systems on AWS
Junior AI/ML Engineer specializing in LLM applications, RAG, and multimodal computer vision
Mid-Level Software Engineer specializing in ML and Generative AI applications
Mid-Level ML/AI Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level AI/ML Engineer specializing in risk modeling, healthcare analytics, and MLOps
Mid-Level Software Engineer specializing in cloud microservices and AI automation
Executive Fractional CTO specializing in multi-cloud, data platforms, and AI/ML architectures
Tony Barreto
Mid-Level Full-Stack Software Developer specializing in modern web apps
“Product-focused full-stack builder who has shipped and operated multiple production apps from scratch, including an e-commerce bakery delivery scheduler (with concurrency controls and timezone handling) and a real-time passenger music-request system for Lyft rides that hit and resolved YouTube API rate-limit scaling issues via debouncing and caching. Strong in React+TypeScript and Node.js/TypeScript backends, with solid PostgreSQL/PostGIS data modeling and performance tuning.”
Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”
Pavithran Gnanasekaran
Junior AI/ML Engineer specializing in RAG, LLM apps, and cloud-native data platforms
“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”
Junior Software Engineer specializing in Python automation and full-stack web development
Mid-level Full-Stack/AI Engineer specializing in LLM microservices, RAG, and data pipelines
Mid-Level Full-Stack Software Engineer specializing in healthcare web apps and LLM integrations
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Junior Generative AI Engineer specializing in LLM systems and RAG
Mid-level AI Engineer specializing in LLM agents, RAG, and evaluation
Mid-level Software Engineer specializing in Python automation and GenAI on AWS
Haeshitha Indukuri
Entry AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”
Junior Software/AI Engineer specializing in LLM agents and RAG systems
Sarang Pratham
Junior Software Engineer specializing in data engineering and GenAI
“Built and deployed a production LLM-powered recruitment chatbot that automates key recruiting steps (sourcing, candidate engagement, screening). Strong in agent orchestration with LangGraph, including guided graph-based workflows, context-aware routing, and reliability measures like clarifying steps plus human-in-the-loop evaluation.”