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Anirudh Sridhar

@anirudhsridhar

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Machine Learning Engineer at New Relic building LLM evaluation and RAG systems, improving Retrieval Recall@5 by 40%.

India

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At New Relic, I architected an MLOps system that uses Airflow for daily evaluation and retraining, tracks experiments in MLflow, and serves models through a FastAPI endpoint. I also improved Retrieval Recall@5 by 40% and Answer Precision by 25% in the RAG pipeline.

I built a centralized LLM Gateway at Outsystems for secure, observable access to internal models, and automated infrastructure provisioning with Terraform. At Mad Street Den, I improved computer vision model accuracy on client data from 80% to 95%.

Experience

Work history, roles, and key accomplishments

Current

Machine Learning Engineer 2

Current

New Relic

Jul 2024 - Present (2 years 3 months)

Architected an end-to-end MLOps system using Airflow and MLflow, and designed a Data Retriever agent with dual-mode APIs. Implemented LLM evaluation frameworks and Knowledge Augmented Generation techniques, improving retrieval recall by 40% and answer precision by 25%.

Python Machine Learning AWS LLM MLOps RAG Airflow MLFlow CI CD Docker Kubernetes Terraform VLLM Grafana Agentic AI LangGraph Lang Chain fastAPI Amazon SQS Lambda

Machine Learning Engineer

OutSystems

Jul 2023 - Jul 2024 (1 year)

Engineered a middleware layer to capture request/response payloads for RLHF datasets and built a centralized LLM Gateway for security and observability. Automated infrastructure provisioning using Terraform and optimized CI/CD pipelines.

Python Machine Learning LLM MLOps Terraform CI CD RLHF Security Observability

Junior Machine Learning Engineer

Mad Street Den

Jul 2022 - Jul 2023 (1 year)

Improved computer vision model accuracy on client data from 80% to 95%. Used PySpark to fetch client data from delta tables, improve models, deploy at scale, and monitor traffic in Grafana.

Python Machine Learning Computer Vision PySpark Delta Lake Grafana Deployment Monitoring