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
Product BuildingEducation · Ethics · Career AI
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
Education & Academic Foundation
The computer science foundation behind the AI products I build.
My graduate work at Pace University combined computer science foundations with AI, data, systems, networking, computing infrastructure, project delivery, and AI ethics.
Graduate Education
Master of Science in Computer Science
Pace University
Seidenberg School of Computer Science and Information Systems
Certified Electronic Diploma
Pace University issued the degree as a certified electronic credential.
Graduate coursework
What I studied—and how I connect it to my work today.
CS 696C
Responsible AI
AI Ethics
A central part of my academic foundation in thinking critically about the human and societal consequences of AI.
Connection to my work
Connects directly to my work on EthicLens AI, human-centered AI research, responsible deployment, fairness, privacy, transparency, accountability, and human oversight.
CS 627
AI Foundations
Artificial Intelligence
Graduate study focused on the foundations of artificial intelligence and computational approaches to intelligent systems.
Connection to my work
Supports my work with generative AI, LLM applications, AI agents, evaluation workflows, and intelligent product development.
CS 619
Data & Machine Learning
Data Mining
Graduate-level study centered on extracting useful information and patterns from data.
Connection to my work
Relevant to data preparation, AI evaluation, retrieval systems, knowledge pipelines, and data-driven product decisions.
CS 691
Product Delivery
Computer Science Capstone Project
A graduate capstone focused on applying computer science knowledge through a substantial team project.
Connection to my work
This became WePOS, where I worked as Project Manager across sprint planning, AWS environments, APIs, CI/CD, documentation, architecture, testing, and delivery.
Additional CS foundation
CS 610 · Scalable Computing
Introduction to Parallel Computing
Strengthens how I think about performance, scalability, distributed workloads, and production AI infrastructure.
CS 612 · Internet Systems
Concepts & Structures: Internet Computing
Connects to my work with web applications, APIs, cloud-hosted AI products, client-server systems, and application architecture.
CS 633 · Networks
Data Communications & Networks
Provides infrastructure context for cloud applications, APIs, distributed AI services, and networked software systems.
CS 623 · Data Engineering
Database Management Systems
Relevant to SQL, application backends, vector-enabled data systems, Supabase, retrieval workflows, and persistent AI application state.
CS 608 · Computer Science Foundations
Algorithms & Computing Theory
Supports structured problem solving, efficiency analysis, system design, and reasoning about computational tradeoffs.
CS 604 · Systems
Computer Systems and Concepts
Supports my understanding of how applications interact with underlying compute environments and infrastructure.
From classroom to product
Turning academic foundations into systems I can actually build.
AI EthicsResponsible AIEthicLens AI
AI + Data MiningIntelligent SystemsAI Products
Internet + NetworksAPIs + CloudDeployed Apps
CS CapstoneProduct DeliveryWePOS
AI Products
Building practical AI products around ethics, education, and professional development.
My product work combines AI engineering, Responsible AI research, product strategy, evaluation, and cloud deployment.
Flagship Product
Responsible AI Platform
EthicLens AI
An AI ethics auditing platform that helps teams evaluate AI systems across fairness, privacy, transparency, explainability, hallucination, safety, human oversight, governance, and compliance risk.
I am building EthicLens AI to translate Responsible AI principles into a practical assessment workflow. Users describe an AI system, its purpose, data usage, decision process, and deployment context. EthicLens then generates a structured risk assessment with identified concerns, severity levels, recommended controls, and areas requiring human review.
What it evaluates
Bias and fairnessPrivacy and data useTransparencyExplainabilityHallucination riskHuman oversightAI safetyGovernance
How I am building it
Designing a structured Responsible AI assessment framework
Creating prompts for risk classification and control recommendations
Developing explainable reports instead of simple risk scores
Adding human-review points for high-risk AI decisions
Connecting ethical principles with technical and governance controls
Deploying and iterating the application through Google Cloud Run
Technical foundation
GeminiPrompt EngineeringResponsible AIRisk AnalysisCloud RunAI Governance
Responsible AI foundation
Applying ethical AI knowledge developed through research and work with Professor James Brusseau.
My approach to EthicLens is informed by my study of AI ethics and my work with Professor James Brusseau on Caffeinated Professor and related research. That experience strengthened my understanding of autonomy, dignity, fairness, privacy, transparency, explainability, accountability, human oversight, and the social impact of AI.
Rather than treating AI ethics as a checklist, I use these principles to examine how an AI system affects real people, how decisions are explained, where human judgment is required, and what controls should exist before and after deployment.
Additional products
More AI applications in development.
Research Product
AI Education
Caffeinated Professor
A mimetic AI teaching platform that transforms faculty knowledge, lectures, course materials, and teaching style into grounded, accessible learning support.
Generative AIRAGOpenAIEmbeddingsSupabase
Detailed project belowView details
In Development
AI Portfolio Builder
BuildFolio
An AI-powered portfolio platform designed to help professionals transform their experience, projects, skills, and goals into a structured digital portfolio.
Next.jsGenerative AITypeScriptPrompt Engineering
Demo coming soon
Planned
Career Intelligence
PathPulse
An AI career path and certification navigator that identifies skill gaps, recommends learning paths, compares certifications, and supports career planning.
Career AIRecommendationsSkill AnalysisLLMs
Coming soon
Planned
AI Career Platform
BigLeap
An AI career accelerator designed to connect professional goals, technical skill development, project readiness, and personalized job preparation.
Agentic AICareer PlanningPersonalizationAutomation
Coming soon
Flagship education 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.