About

Engineering thoughtful ML/AI systems with a human-first lens.

I like turning complex AI workflows into calm, intuitive experiences. My work blends research discipline, product intuition, and a focus on the engineering details that keep models reliable in production.

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Approach

Curious, careful, reliable

I focus on clarity in the UI, resilience in the system, and warmth in the experience.

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Working style

Systems thinking with a soft edge.

Full-stack ML/AI engineering

I’m Tanmay Dogra, a full-stack ML/AI engineer who specializes in building models, training pipelines, and production systems that bring ideas into working products. I focus on the full lifecycle: data ingestion, experimentation, deployment, and the interfaces people use to trust the results. I care about reliability in the real world, so I design for monitoring, latency, and scale from the start. The goal is simple: turn strong research into software that teams can ship, maintain, and grow.

Production scaling

I focus on reliability and performance, using monitoring and iteration to scale AI products from prototype to steady, production-grade delivery.

Collaboration

I communicate early and often, keeping stakeholders aligned while making space for crisp feedback loops and thoughtful execution.

Skills & Technologies

AI/ML engineering toolkit

Languages

  • Python
  • C++

ML/DS Libraries

  • NumPy, SciPy, Pandas
  • Scikit-learn
  • Matplotlib, Seaborn

Analytics Stack

  • ggplot2, caret
  • dplyr, purrr, readxl

GenAI / LLM Tooling

  • OpenAI, Anthropic
  • Hugging Face, LangChain
  • Ollama

Cloud & MLOps

  • AWS (EC2, SageMaker)
  • Azure, GCP

Big Data & Streaming

  • Hadoop, Hive, HDFS
  • MapReduce, Kafka

Data Modeling & BI

  • Erwin, Rational Rose
  • ER/Studio, MS Visio
  • SAP PowerDesigner, Tableau

Web & Integration

  • HTML5, DHTML, XML
  • Web Services