Medhaja H L · Bengaluru, India

I build AI products from idea to deployment.

I’m a GenAI Technical Lead and AI product builder, combining product thinking, full-stack engineering, and production GenAI systems. I’m also the creator of Isocline, an open-source platform for building agent workflows.

Open to Staff / Lead GenAI opportunities and consulting engagements.

Explore my work Get in touch
Work

Featured product

Name: Medhaja H L

Profile: Senior Software Engineer (Gen AI/ MLOps)

Email: medhajahlbhat@gmail.com

Skill

Python 85%
C 75%
Java 50%
Who it’s for
Developers and teams who want to compose agent workflows visually and run them on their own infrastructure with any model provider. A Windows desktop build and 53 ready-to-run templates also make it usable by individuals.
My role
I created Isocline and develop it: architecture, runtime, web builder, SDK and CLI, desktop packaging, and release verification.
What it does today
  • Visual builder with typed contracts between nodes, checked before a run starts
  • Durable execution on PostgreSQL: parallel DAGs, crash recovery, checkpoints, rerun from any node
  • Human-in-the-loop waits (approval, timer, callback, event) that hold no worker
  • Saga-style compensation that undoes side effects in reverse order
  • Sandboxed Python in disposable, network-less containers
  • Any model: OpenAI, Anthropic, Gemini, OpenRouter, Ollama and OpenAI-compatible servers
  • MCP tools, RAG on pgvector, webhook and schedule triggers, REST API, Python SDK and CLI
Read the case study Source on GitHub Documentation Windows installer
Earlier ML work

Smaller projects with public code

Personal learning projects from before Isocline. They are experiments, not production systems.

2024 · LLM evaluation

Evaluating a RAG question-answering pipeline with MLflow

Problem
Measure the answer quality of a retrieval-based QA pipeline instead of judging outputs by eye.
What I did
Built a LangChain RetrievalQA pipeline over a PDF with a FAISS index, then scored it with mlflow.evaluate using LLM-judged faithfulness and relevance metrics plus latency. Packaged with Docker.
Stack
Python · LangChain · FAISS · OpenAI · MLflow · Docker
Learning
Wiring LLM-as-judge metrics into experiment tracking. The evaluation set is four questions, so this is a pattern exercise, not a benchmark.
Source
2024 · Tabular ML

Heart failure prediction with XGBoost and MLflow

Problem
Predict a death event from 13 clinical features in a public Kaggle dataset of heart-failure records.
What I did
Trained an XGBoost classifier, ran a 50-trial Hyperopt search with every trial logged as a nested MLflow run, and evaluated with accuracy, ROC AUC, a confusion matrix and feature-importance plots.
Stack
Python · XGBoost · Hyperopt · scikit-learn · MLflow
Learning
Using MLflow to make hyperparameter search reproducible and comparable across trials.
Source
2023 · Computer vision

In-browser image classifier with transfer learning

Problem
Classify two product types from a webcam image entirely on the user’s device, without sending images to a server.
What I did
Fine-tuned a VGG16 model, converted its weights for TensorFlow.js, and built a static page that classifies webcam captures in the browser.
Stack
TensorFlow / Keras · VGG16 · TensorFlow.js · static hosting
Result
A working demo that runs on-device. The demo asks for webcam access.
Live demo Source
Experience

Where I’ve worked

  1. – Present

    GenAI Technical Lead Current

    Biz-Metric India Pvt. Ltd.

    • Lead delivery of GenAI applications.
    • Work across architecture, implementation, evaluation and deployment.
    • Guide engineers and collaborate with stakeholders.

  2. –

    Senior Software Engineer

    Infinite Computer Solutions

  3. –

    Software Engineer, AI/ML

    SourceFuse

  4. –

    Analyst / Data Scientist

    EXL

Capabilities

What I work on

GenAI systems

RAG, agent workflows, tool use, structured extraction, LLM evaluation.

Evidence: Isocline · RAG evaluation with MLflow

5

TOTAL INDUSTRY EXPERIENCE

Inference and deployment

vLLM, Docker, Kubernetes, cloud deployment.

Evidence: Isocline’s Docker Compose stack and sandbox

Reliability

Tracing, failure handling, evaluation pipelines, latency and cost analysis.

Evidence: Isocline’s durability checks and benchmarks

Product and leadership

Problem definition, prototyping, technical design, delivery, mentoring.

Evidence: Current technical lead role

About

Engineer, technical lead, product builder.

I’m a GenAI Technical Lead based in Bengaluru, with a background in Computer Science and Engineering from MIT Manipal.

My work combines hands-on engineering, technical leadership, and product development. I enjoy taking an ambiguous business problem, building a useful prototype, and developing it into a dependable application.

Alongside my professional work, I build Isocline. I’m interested in opportunities where I can own AI products, work closely with users, and lead delivery from concept to deployment.

Background

Education & Certifications

Education

B.Tech in Computer Science and Engineering Manipal Institute of Technology, Manipal
2015 – 2019

Certifications

Work with me

Let’s build something useful.

For hiring

Staff / Lead GenAI roles

Interested in Staff / Lead GenAI and hands-on AI product engineering opportunities.

For consulting

GenAI applications

I help teams prototype and build AI workflow automation, knowledge assistants, and GenAI applications.

Email medhajahlbhat@gmail.com