.png){kind=logo}
Vetted Prompt Engineering Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Executive Technology Leader specializing in regulated multi-tenant SaaS and LLM-enabled products
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and LLM applications
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Mid-level AI/Data Engineer specializing in LLMs, RAG pipelines, and cloud data platforms
Mid-level Data Scientist specializing in GenAI, NLP, and cloud MLOps
Junior Data & AI Analyst specializing in BI, LLM applications, and analytics
Mid-level Full-Stack .NET Developer specializing in Angular and ASP.NET Core APIs
Mid-level Data Engineer specializing in cloud data platforms and BI analytics
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Applied AI Engineer specializing in Generative AI and RAG systems
Engineering Leader specializing in FinTech, payments, and enterprise platforms
Tushar Jayendra Mhatre
Intern Data Scientist/ML Engineer specializing in generative AI and ML platforms
“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”
Tushar Gwal
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and MLOps
“AI engineer with experience taking a GPT-4-powered GenAI career coach toward production on Azure AI Foundry, re-architecting the backend with hybrid (vector + keyword) search and RAG optimizations to cut latency by 50%. Also has client-facing TCS experience building healthcare ETL pipelines and delivering error-free monthly reports, plus current work analyzing agentic system reasoning traces and guardrail drift as an AI research fellow.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
Praniket Ketan Walavalkar
Junior AI Software Engineer specializing in RAG agents and cloud data platforms
“AI Software Engineer (student employee) at University of Washington IT who helped deploy "Purple," a governed, explainable LLM platform on Azure used by 100,000+ students/faculty/staff. Independently led scalable reliability efforts by building automated agent quality/load/red-team testing and CI/CD health validation (Playwright/Node.js, Azure DevOps), and previously built an explainable AI scheduling assistant for clinical operations at Proliance Surgeons.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
William Armstrong
Junior Product Engineer specializing in AI and SaaS
“Product intern at an AI startup (AdvisorGPT) who helped turn an LLM-based prototype into a production SEO blog-generation workflow that matched a firm’s tone/voice and targeted specific search phrases. Strong at bridging technical and non-technical teams, rapidly learning new AI tooling, and driving adoption through customer calls, UX improvements, and customer-facing demos/workshops.”
Nakul Reddy Sarasani
Junior Full-Stack Software Engineer specializing in cloud-native distributed systems
“Software engineer with JPMorgan Chase experience building a real-time operations console backend on Spring Boot/Kafka/Kubernetes and resolving peak-load latency through profiling, indexing, caching, and async processing. Also built and owned an AI-driven digital-archives metadata pipeline during a master’s at UNT using OCR + LLaMA-based prompting with validation, near-human accuracy, and human-in-the-loop guardrails.”