Years in Engineering
12+
Building software, automation, and platform systems
bharad@portfolio:~
$whoami
Bharadwaj Pendyala
> |
Senior software engineer at Salesforce focused on enterprise communication systems, AI-enabled workflows, and pragmatic architecture that helps teams ship reliably.
Snapshot
- Experience: 12+ years
- Current role: SMTS at Salesforce
- Focus: Platform systems and AI workflows
- Primary stack: TypeScript, Python, AWS serverless
$open ~/quick_actions
$ls ~/social
$Ready for the next command_
$cat metrics.json
Results at a glance
Selected highlights from professional engineering work.
Years at Salesforce
5+
Growing into lead-level engineering ownership
Current Scope
Org-wide
Workflow platforms, delivery enablement, and AI adoption
Public Builds
3
Portfolio, Jarvis, and a document-aware RAG system
$ls -la ~/portfolio
Explore by section
Jump directly to projects, writing, resume, and background with faster navigation cards.
$cat ~/posts/latest.md
Latest writing
Notes on AI, engineering, and things worth figuring out.
July 3, 2026•15 min read
RAG: How an LLM Answers From Documents It Never Saw
A chat model only knows what it saw in training. RAG is the plumbing that lets it answer from your private documents, today's news, or anything it was never trained on: a second kind of model that turns text into coordinates, a database of those coordinates, and a search step that runs before the LLM ever sees your prompt.
June 25, 2026•16 min read
From Transformer to ChatGPT: The Part That Isn't the Architecture
A stack of transformer layers is not yet ChatGPT or Claude. This is the rest of the path: how text becomes tokens, how a raw next-word predictor turns into an assistant across three training phases, how LoRA customizes a model on a budget, and why everyone is racing for data and compute.
$cat summary.md
Current focus and impact
Public-safe themes from platform engineering, AI workflow work, and cross-team delivery.
$cat focus-areas.md
What I build
- •Enterprise workflow and communication systems that support high-trust delivery.
- •AI-enabled internal tools that reduce manual coordination and improve usability.
- •Platform migrations that improve reliability, observability, and operational clarity.
- •Reusable engineering patterns that help teams ship faster without losing quality.
$grep -i "impact" delivery.log
How I deliver
- •Take ownership of ambiguous platform problems and turn them into shippable systems.
- •Bridge product, engineering, and stakeholder needs with practical technical decisions.
- •Lead rollout readiness through testing, incident response, documentation, and support.
- •Apply AI tooling in ways that improve real engineering velocity instead of adding noise.