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Manikanta Kadiyam

Mid-level Applied AI Engineer specializing in agentic LLM workflows

VerizonUniversity of HoustonIrving, TX5 Years ExperienceMid LevelWorks On-Site

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About

Master’s-in-Data-Science candidate (UHV) with 4+ years in AI engineering building production LLM and multimodal systems. Designed an LLM-powered workflow automation platform using RAG over vector stores with guardrails (schema/output validation, fallbacks) and a rigorous evaluation/monitoring framework including drift tracking and shadow deployments. Experienced orchestrating large-scale vision-language pipelines with Airflow and Kubernetes (OCR, distributed training) and partnering with non-technical ops stakeholders to cut cycle time and reduce errors.

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Key Strengths

  • End-to-end AI/ML delivery (data engineering → model development/optimization → deployment/monitoring)
  • Production LLM workflow automation with RAG and API-triggered downstream actions
  • Reliability/guardrails for LLMs (schema + output checks, fallback templates) to reduce hallucinations
  • Strong evaluation discipline across model + system metrics (precision/recall, drift, retrieval quality, latency/throughput/cost)
  • Shadow deployment strategy to compare models safely before full rollout
  • Orchestrating complex multimodal pipelines with Airflow DAGs and Kubernetes (OCR, data cleaning, batch generation, distributed training, evaluation)
  • Translating non-technical stakeholder domain knowledge into deployable AI solutions; improved process time and reduced errors

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Experience

Applied AI Engineer – Agentic Systems & LLM WorkflowsVerizon · Jun 2023 – Present
Machine Learning Engineer – LLM Platforms & Enterprise AutomationMphasis · Jun 2021 – Jul 2022
AI Engineer – NLP Automation & Decision SystemsInfovision Technologies · Jan 2020 – May 2021

Education

University of Houstonmaster, Data Science
Bharath Institute of Higher Education and Researchbachelor, Aerospace Engineering

Languages

English

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Manikanta KadiyamMid-level Applied AI Engineer specializing in agentic LLM workflows