Shreyash Pandey // Software Development Engineer 2 at IBM

I build AI systems, automation workflows, and backend products that survive production

My recent work sits where applied AI meets engineering discipline: locator auto-healing, semantic retrieval, internal copilots, multilingual model training, and automation pipelines with real operational guardrails.

  • 5K+ test locators auto-healed
  • 3-tier ML plus VLM recovery design
  • Hackathon-winning semantic search
  • Open-source model training from scratch
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Current proof surface

The strongest signal in the portfolio is not a single project. It is the combination of AI recovery design, service reliability, and hands-on model building shipped across work and independent systems.

Recovery ladder
3 tiers
CSS selectors first, then embeddings, then IBM Granite 3.3 VLM when ambiguity stays high.
Runtime scale
4K-5K rpm
SAT runtime decomposed into four services with sub-second latency and release-grade operational constraints.
Model track
125M
Phoenix 125M plus multilingual pretraining work built with custom tokenizers and training pipelines.
5K+ test locators auto-healed3-tier ML plus VLM recovery designHackathon-winning semantic searchOpen-source model training from scratch
5K+ test locators auto-healed
4 microservices in the SAT runtime
83% Chrome accuracy in fallback mode
70% fewer internal support tickets
99.99% availability supported by prediction
1st place in India at TechInterrupt

What I optimize for

The common thread across my work is reliability under ambiguity. I like systems that need to reason, recover, and still stay operationally legible.

Start with the cheapest reliable fallback

I design systems to attempt deterministic recovery first, then graduate into ML and model-based fallbacks only when they are justified.

Treat evaluation as part of the product

When the system contains AI, the measurement loop is not optional. I care about observable accuracy, drift, error budgets, and failure analysis.

Systems I have shipped

2024 - Present

Software Development Engineer 2

IBM Software Labs · Bengaluru, India

I design and ship reliability-heavy AI capabilities inside browser automation and testing products. The work combines embeddings, vision-language models, service decomposition, and a lot of operational discipline.

2023 - 2024

Software Engineer 2

Software AG (now IBM) · Bengaluru, India

This phase pushed me deeper into AI product work: semantic retrieval, internal copilots, and prediction systems grounded in practical product needs rather than demos.

2022 - 2023

Software Engineer

Software AG · Bengaluru, India

I worked on enterprise integration platform capabilities across Java, Spring Boot, and REST APIs, building the foundation that still shapes how I reason about production systems.

Current projects

2026 · Decoder-only language model

Phoenix 125M

A LLaMA-style 125M parameter model trained from scratch on a single RTX 3080 Ti with a custom tokenizer, data pipeline, and training loop.

PyTorchTransformersTokenization
View model card

2026 · Multilingual language models

Sweta-Hi and Sweta-Kn

Hindi and Kannada pretraining efforts built on a LLaMA-style architecture with custom tokenizers and an end-to-end multilingual data pipeline.

Multilingual NLPData engineeringCustom tokenizers
View model card

2026 · Agentic content pipeline

LinkedIn Post Swarm

A multi-agent publishing workflow that uses Claude, Ollama, Playwright, and Telegram for draft generation, review, approval, and scheduled publishing.

Agent orchestrationPrompt engineeringPlaywright

See all builds

Explore the full project portfolio

Looking for my next role

AI Systems Engineer based in Bengaluru, India

I am most interested in roles where AI systems, backend engineering, and reliability work intersect. That usually means agent infrastructure, evaluation-heavy product work, automation platforms, or developer tooling.

Get in touch More about me