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Vetted Teaching Assistants in the DFW Metroplex
Pre-screened and vetted in the DFW Metroplex.
Ajith Kumar
Mid-level AI Data Engineer specializing in GenAI, RAG, and cloud data pipelines
“LLM/agentic AI builder who deployed a production ITSM automation agent on Google ADK integrating ServiceNow and FreshService, with strong safety guardrails (human-approval gating and runbook-only command execution) and rigorous evaluation (500 synthetic tickets; 80%+ false-positive reduction). Also partnered with finance to deliver an AI agent that automated invoice/SOW retrieval and monthly reporting to account managers, reducing manual back-and-forth.”
Mid-level Machine Learning Engineer specializing in reinforcement learning and autonomous systems
Mid-level Full-Stack Engineer specializing in cloud-native and Generative AI systems
Utkarsh Vijayvergia
Entry-level Software Engineer specializing in full-stack, cloud, and AI systems
“Builder with hands-on experience shipping full-stack products across AWS cloud infrastructure, React/TypeScript apps, SQL-backed systems, and privacy-focused AI workflows. Stands out for combining cost-aware architecture, strong debugging instincts, and product thinking—from an e-commerce platform automated with IaC to a university admin portal serving 10,000+ users and a locally run AI assistant with configurable guardrails.”
Mid-level Software Systems Engineer specializing in cloud infrastructure and AI applications
Intern Full-Stack Software Engineer specializing in web development, data pipelines, and cloud
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).”