Sreekar Reddy Portfolio
Applied AI Engineer (ex-IBM, building internal AI platforms at Pretian Squared) whose portfolio shows five distinct shipped RAG/agent projects, including a RAG system with published faithfulness and answer-relevancy scores and a multi-agent code-review tool with six specialist agents.
Location
Not listed — probably too busy shipping to update the bio.
Skills
What makes it work
A breakdown of the choices that make this portfolio stand out.
RAG quality reported as named, specific metrics
Citing faithfulness (0.68 to 0.84) and answer-relevancy (0.72 to 0.91) scores for the CyberRAG project gives a concrete, evaluable measure of retrieval quality instead of a vague "built a chatbot" claim.
Multiple small, distinctly-named agent projects instead of one flagship
Five separate named tools — a RAG system, a code-verification layer, a browser-native model runtime, a multi-agent review council, and a P2P video app — show range across the applied-AI stack rather than one narrow demo.
A specialist-agent structure named explicitly
Describing the code-review tool as a "council" of six specialized agents, rather than one generic assistant, shows deliberate multi-agent architecture thinking, not just a single wrapped LLM call.
What AI Engineers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Report actual evaluation metrics (faithfulness, relevancy scores) for any RAG system you've built — it's far more convincing than describing it as "accurate."
Ship several small, distinctly named agent projects rather than one large unfinished one — range across the stack demonstrates versatility.
If a project uses multiple cooperating agents, name their distinct roles explicitly — it shows you're designing an architecture, not prompting a single model.
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