Generative AI
OpenAI · Claude · Gemini · Llama · Prompt Engineering
AI Engineer · Founder · Researcher
Building AI that is intelligent, trustworthy, and human-centered.
I'm Anirudh Kolanupaka, an AI engineer and founding engineer working across generative AI, agentic systems, RAG, LLM evaluation, educational technology, and Responsible AI.
AI EngineeringLLMs · Agents · RAG · APIs
LeadershipFounder · Project Manager · Product
Research FocusResponsible and educational AI
About
I build AI systems and study the responsibilities that come with them.
My work sits at the intersection of engineering, product development, and AI ethics. I build production-oriented applications using large language models, retrieval-augmented generation, intelligent agents, semantic search, APIs, and cloud infrastructure.
Alongside engineering, I study fairness, transparency, privacy, explainability, human oversight, trustworthy AI, and responsible deployment. My goal is to help create systems that deliver measurable value while preserving human dignity, judgment, and accountability.
Core capabilities
Engineering depth with Responsible AI thinking.
Agentic Systems
LangGraph · LangChain · CrewAI · AutoGen · MCP
RAG & Search
Hybrid RAG · Embeddings · FAISS · Pinecone · ChromaDB
AI Engineering
Python · FastAPI · SQL · TypeScript · REST APIs
Cloud & MLOps
AWS · GCP · Azure · Docker · MLflow
Responsible AI
Fairness · Explainability · Safety · Governance · Evaluation
Flagship work
Caffeinated Professor
An AI-powered educational platform that extends a professor's knowledge, teaching style, and guidance into accessible, 24/7 learning support.
The challenge
Professor availability is limited, course knowledge is spread across lectures and materials, and many students hesitate to ask questions during traditional office hours.
The approach
Caffeinated Professor combines transcription, structured knowledge preparation, embeddings, semantic retrieval, large language models, evaluation, and voice technology to create grounded educational interactions.
OpenAIRAGWhisperSupabaseRedisGCPElevenLabs
Course media
Transcribe & prepare
Embed & retrieve
Generate & evaluate
What I do
Engineering the platform while helping shape the product and research direction.
As a founding engineer, I own major parts of the data, retrieval, evaluation, and documentation workflow. I work with faculty and technical collaborators to turn academic knowledge into a reliable AI learning experience.
Designing the end-to-end AI architecture and technical roadmap
Building RAG pipelines grounded in lectures, course material, and faculty knowledge
Leading data preparation, transcription, chunking, embeddings, and retrieval workflows
Developing evaluation methods for usefulness, accuracy, grounding, hallucination, and clarity
Improving prompts and retrieval strategies to reproduce the professor’s teaching voice responsibly
Collaborating with faculty and engineering teammates on product strategy, pilots, and deployment
Translating Responsible AI principles into practical product controls and human oversight
Preparing technical documentation, research evidence, case logs, and stakeholder updates
Knowledge pipeline
Transforming lecture recordings and course resources into searchable, structured knowledge for grounded generation.
Evaluation framework
Assessing usefulness, retrieval, grounding, correctness, clarity, hallucination, latency, and technical quality.
Responsible design
Embedding transparency, source grounding, human oversight, academic integrity, and educator augmentation into the product.
Product leadership
Supporting feature prioritization, stakeholder communication, technical specifications, pilots, deployment planning, and roadmap decisions.
Capstone project
WePOS
A cloud-based restaurant point-of-sale platform designed to unify menu management, order processing, third-party delivery integrations, and operational analytics.
The product
WePOS gives restaurant teams a centralized web application for managing menus, incoming orders, customers, and performance insights while supporting delivery-platform integrations through middleware and mock APIs.
The delivery model
The project was developed through an Agile capstone process with sprint planning, Jira tracking, architecture documentation, DEV and QA environments on AWS, and Jenkins-based CI/CD workflows.
AWS EC2AWS CognitoJenkinsJiraCI/CDREST APIsUMLERD
Restaurant team
Web POS
API & middleware
AWS environments
My role
Leading project execution while coordinating architecture, delivery, and team alignment.
As Project Manager, I organized the team's work across sprints, translated requirements into actionable tasks, tracked risks and dependencies, and kept the technical implementation aligned with the capstone scope and deadlines.
Led sprint planning, backlog prioritization, task ownership, and milestone tracking
Coordinated developers, AWS administration, Jenkins ownership, QA, and documentation activities
Facilitated stand-ups, progress reviews, sprint demonstrations, and team communication
Oversaw system design artifacts including UML, data-flow diagrams, and the database ERD
Supported cloud architecture, authentication, API integration, testing, and release planning
Managed project risks, scope decisions, blockers, and delivery expectations
Prepared project documentation and presentations for academic stakeholder reviews
Agile leadership
Managed multiple sprints with clear priorities, ownership, progress tracking, reviews, and retrospectives.
Cloud delivery
Coordinated separate AWS EC2 development and QA environments with AWS Cognito authentication.
DevOps workflow
Helped organize Jenkins pipelines for automated DEV and QA deployment with team notifications.
System planning
Connected product requirements with architecture, integrations, testing strategy, documentation, and release execution.
Research & knowledge
Exploring what trustworthy AI requires in practice.
Responsible AI
Connecting fairness, accountability, transparency, explainability, safety, and human oversight to real product decisions.
GovernanceBiasSafetyTrust
Human-Centered AI
Studying how AI can augment human capability while preserving autonomy, dignity, agency, and informed judgment.
Human OversightAutonomyDignity
Ethical Educational AI
Exploring consent, disclosure, pedagogical trust, academic integrity, and accountability in mimetic AI tutors.
EdTechMimetic AITrustworthy LLMs
LLM Evaluation
Designing practical evaluations for retrieval quality, groundedness, hallucination, harmful outputs, clarity, and usefulness.
EvalsRAGHallucination
AI ethics knowledge base
Topics I study, apply, and write about.
My work connects philosophical principles with technical controls, product decisions, evaluation methods, governance processes, and real-world implementation.
FairnessTransparencyPrivacyExplainabilityAI SafetyGovernanceHuman OversightAccountabilityRobustnessAlignmentTrustworthy AISocial Impact
Research output
Work at the intersection of technology, ethics, and education.
Research manuscript · In progress
Human-Centered AI: A Framework for Normalizing Ethical AI Use in Daily Life
Proposing a practical framework for integrating ethical AI into everyday life through human-centered design, transparency, accountability, trust, autonomy, fairness, and responsible human-AI collaboration.
Forthcoming research manuscript
Ethics and Technology of the Mimetic AI Professor
Examining the design, educational value, risks, and ethical implications of an AI system that reproduces aspects of a professor's pedagogical identity.
Forthcoming book chapter
Design Thinking and Artificial Intelligence: Redefining Pedagogy for the Digital Age
Contributing to research on AI, design thinking, learning, human-centered innovation, and the future of education.
AI should augment human capability, preserve human judgment, and earn trust through transparency, accountability, and responsible design.
Contact
Let's build AI that works—and deserves to be trusted.
Open to AI engineering, generative AI, agentic systems, Responsible AI, research collaboration, and startup opportunities.