AI Infrastructure · ML Platforms · Data Platforms · Production Scale
I'm Shashank, and I build the unglamorous-but-essential plumbing behind modern AI — the recommendation engines, the data pipelines, the GenAI infrastructure. The stuff that processes tens of terabytes a day and serves hundreds of millions of users. If that sounds like your kind of conversation, you're in the right place.
30+
TB / day processed
115M+
users supported
20+
ML strategies
35%
compute reduction
About
With 10+ years of experience, I design and own production-scale systems spanning ML platform engineering, distributed Spark pipelines, recommendation infrastructure, cloud-native data platforms, and enterprise LLMOps. I focus on architecture that balances velocity, reliability, and cost at global scale.
30+TB/day
Data processed daily
115M+
Customers supported
20+
Recommendation strategies
35%
Compute reduction
60%
Faster onboarding
Featured Projects
Large-scale case studies across recommendation platforms, vector search, GenAI workflows, and enterprise data modernization.
Case Study 01
Configuration-driven personalization at global scale
Architected a configuration-driven recommendation platform powering 20+ personalization strategies across Walmart International markets.
Case Study 02
Embedding-based retrieval at production scale
Built embedding-based retrieval pipelines using multimodal AI models for semantic recommendation systems.
Case Study 03
GenAI workflows with intelligent reranking
Designed GenAI recommendation workflows using LLM-generated complementary queries and intelligent reranking systems.
Case Study 04
Teradata to BigQuery at 115M+ customer scale
Led migration of Verizon revenue recognition systems from Teradata to BigQuery for 115M+ customers.
Case Study 05
Enterprise-grade financial data validation
Built enterprise reconciliation systems validating consistency across multiple financial data layers.
AI & Agentic Systems
From Cursor-assisted development to autonomous monitoring agents and production LLMOps — building systems where AI amplifies engineering velocity without sacrificing reliability.
Accelerating platform delivery with Cursor, Claude, and Codex workflows integrated into daily development.
Structured prompt templates, evaluation harnesses, and versioned prompt registries for production LLM features.
End-to-end observability for embeddings, retrieval, reranking, cost tracking, and quality regression gates.
Agents that detect pipeline drift, SLA breaches, and data quality anomalies with automated remediation playbooks.
Tracing LLM calls, token economics, hallucination signals, and retrieval hit rates across recommendation surfaces.
Multi-agent workflows for code review, incident triage, and data platform operations with human-in-the-loop gates.
orchestration.status
Multi-agent pipeline active
Technical Expertise
Career Timeline
Technical Writing
Work With Me
Open to senior engineering roles, platform consulting, and collaborations on production AI infrastructure. Book a call or reach out directly.