.png){kind=logo}
Vetted Amazon SageMaker Professionals
Pre-screened and vetted.
Dhairya Gajjar
Mid-Level Software Engineer specializing in Healthcare Data Platforms
“Backend/ML engineer with healthcare domain experience building secure Medicare/Medicaid data APIs and real-time patient risk scoring. Shipped an end-to-end ML pipeline (scikit-learn/XGBoost) served via SageMaker and integrated into Flask APIs, with strong production reliability practices (Kafka schema validation, regression replay, observability, drift monitoring, and human-in-the-loop guardrails).”
Sai Vamsee Bandaru
Entry-level Data Analyst and AI Engineer specializing in machine learning and LLM systems
“Founding-engineer-oriented full-stack product engineer who built an AI tutor system end-to-end, spanning React UI, FastAPI backend, retrieval/LLM pipelines, and Postgres optimization. Stands out for combining product thinking with deep systems work: improving onboarding and activation, shipping quickly with beta users, and abstracting reusable retrieval infrastructure for multiple use cases.”
Karthikeya Arra
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”
Senior Full-Stack Software Engineer specializing in Python microservices and cloud platforms
Mid-level Data Scientist / AI Research Engineer specializing in LLMs, RAG, and applied ML
Mid-Level AI/Full-Stack Engineer specializing in conversational AI and SaaS products
Mid-level Full-Stack/AI Engineer specializing in LLM microservices, RAG, and data pipelines
Mid-level Data Scientist specializing in GenAI, MLOps, and computer vision for robotics
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and Computer Vision
Junior Machine Learning Engineer specializing in healthcare and IT analytics
Junior Machine Learning Engineer specializing in LLMs and multimodal AI
Intern Full-Stack & AI Engineer specializing in ML-driven mobile and data platforms
Mid-level Full-Stack AI Engineer specializing in web and generative AI solutions
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI Engineer specializing in LLM agents, RAG, and evaluation
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Mid-level Software Engineer specializing in Python automation and GenAI on AWS
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
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).”
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps
Muhammad Murtaza Murtaza
Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics
“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”