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ML Engineer / AI Solutions Architect

Krishna MihirTatavarthi

Building ML systems that ship

About

I build AI systems that survive contact with production: LLM agents, RAG pipelines, and the data infrastructure underneath them.

I'm finishing my MS in Computer Science at UMBC (GPA 3.93), where my trajectory has run from classical ML research into cloud-native AI engineering: shipping agentic workflows with LangGraph and LangChain and wiring it all into secure, role-based APIs.

Before the model there is always the data. I've built batch and streaming ETL/ELT pipelines on AWS, orchestrated with Airflow and dbt, and I care as much about observability and data quality as I do about model accuracy. At Date Maroon, improving Mode Analytics dashboards was my daily work. I sat with the real users who depended on them, heard what slowed them down, and turned that feedback into faster queries and clearer reports.

My IEEE-published research on detecting machine-generated text sits right at the intersection I like most: rigorous ML with a real-world stake.

Krishna Mihir TatavarthiRECLIVE
KM.T / PortraitBaltimore, MD
MS Computer Science
UMBC · GPA 3.93 · 2026
Research
2 papers published · IEEE, IJARESM
Based in
Baltimore, MD
Focus
LLM systems · Data platforms · Cloud

Stack

  • LangChain
  • LangGraph
  • AI Agents
  • RAG
  • LLM APIs (OpenAI, Anthropic)
  • Hugging Face
  • PyTorch
  • scikit-learn
  • BERT
  • Pinecone
  • Prompt Engineering
  • PySpark
  • Apache Spark
  • Apache Airflow
  • dbt
  • BigQuery
  • Snowflake
  • PostgreSQL
  • FastAPI
  • ETL Pipelines
  • AWS Glue
  • AWS Athena
  • S3
  • AWS
  • Docker
  • Kubernetes
  • GitHub Actions
  • CI/CD
  • Supabase
  • Linux
  • Git
  • Python
  • SQL
  • TypeScript
  • JavaScript
  • C++
  • React
  • Next.js
  • Node.js

Projects

Indexes a whole codebase, its databases, and its datasets into one evidence-backed knowledge graph, then answers questions about how the system fits together with real file-and-line proof instead of LLM guesses.

  • Full-stack platform (Node.js, Express, React) that statically indexes JavaScript, TypeScript, and Python repositories, SQL schemas, and CSV datasets into a cross-layer knowledge graph, tracing any value across seven layers from a frontend component down to the exact database column, with file and line evidence on every hop.
  • Architected a multi-agent system (LangGraph, LangChain) where a supervisor routes each question to parallel specialist agents for repositories, architecture, and datasets, invoking typed tools validated by Zod schemas.
  • Cut hallucinations with a deliberately non-LLM verification agent that re-checks every claim against the static index before composing an answer, labeling any hop it cannot confirm as unverified rather than inventing it.
  • Deployed to production on Google Cloud Run (Docker, Artifact Registry, Secret Manager) with Firebase authentication and per-user isolated workspaces, engineered safe by construction: a read-only single-SELECT SQL gate, read-only database connections, and sandboxed upload handling.
7
layers traced end to end
3
parallel specialist agents
0
unverified hops passed as fact
Node.jsReactLangGraphLangChainGoogle Cloud RunFirebaseDocker

Experience

  1. Velociti Inc.

    Feb 2026 – May 2026
    Software Engineer Intern · Phoenix, AZ
    • Shipped 10+ production features for a customer-facing web app in React and TypeScript on Supabase and AWS CDK, improving reliability and user-facing performance by ~48%.
    • Built agentic AI workflows with LangGraph, LangChain, and RAG over Gemini and OpenAI-compatible LLMs, generating structured product-strategy artifacts from unstructured input via Supabase Edge Functions and PostgreSQL RPCs.
    • Enforced secure API design across the stack: authentication checks, input validation, and role-based access control.
    ReactTypeScriptLangGraphSupabaseAWS CDK
  2. Date Maroon

    Sep 2025 – Jan 2026
    Software Engineer Intern · Florida, USA
    • Built batch and streaming ETL/ELT pipelines on AWS (Athena, Glue, S3) with Python, SQL, and PySpark, turning raw data into analytics-ready tables stakeholders could trust.
    • Rebuilt the slowest SQL behind Mode Analytics dashboards, making reports load 50% faster while doubling queryable history from 3 to 6 months.
    • Integrated LLM-powered SQL generation (OpenAI API) into pipeline development, cutting exploratory query development time by 60%.
    PythonPySparkAWSSQLMode AnalyticsOpenAI API
  3. UMBC

    Jan 2025 – May 2025
    Graduate Research Assistant · Baltimore, MD
    • Conducted pulsar candidate classification research on the HTRU2 radio telescope survey dataset in coordination with Dr. Milton Halem (formerly of NASA), building an end-to-end ML pipeline with SMOTE-based imbalance handling that lifted recall from 82.3% to 89.6% at 0.97 ROC-AUC.
    • Validated the trained models beyond the benchmark dataset in collaboration with astrophysicist Gnanesh (postdoctoral researcher), who stress-tested them on independent observational data where the models held up at 91% accuracy.
    Pythonscikit-learnPandasSMOTE
  4. Salesforce

    Apr 2023 – Jun 2023
    Virtual Salesforce Developer · Hyderabad, India
    • Built 3+ enterprise apps with Apex, REST APIs, and Lightning Web Components, automating manual workflows; earned Apex Specialist and Process Automation Specialist super badges.
    ApexREST APIsLWC

Contact

Open to ML engineering and AI architecture roles. If you're building something that needs models in production, let's talk.