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Manikanta Kadiyam
Mid-level Applied AI Engineer specializing in agentic LLM workflows
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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.
Experience
Education
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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