# Asrith Reddy V > AI/ML Engineer at Adobe | LLM & Generative AI | MLOps | Transformers | AWS | Scalable ML Systems Location: Harrison, New Jersey, United States Profile: https://flows.cv/asrith AI/ML Engineer with 3+ years of experience building scalable machine learning and LLM-powered systems for enterprise applications. Skilled in developing end-to-end ML solutions including data pipelines, model training, deployment, and production monitoring. Currently working at Adobe on transformer-based recommendation systems and large-scale data pipelines that power personalization across millions of user interactions. Previously at ServiceNow, where I built predictive models and automation systems that improved incident resolution and operational efficiency. Interested in Generative AI, LLM applications, MLOps, and scalable ML infrastructure on AWS. ## Work Experience ### AI/ML Engineer @ Adobe Jan 2025 – Present | United States Partner with product and analytics teams to translate customer engagement challenges into measurable ML objectives, defining success metrics that improved campaign targeting precision by 46% and increased conversion lift by 52% across enterprise accounts. • Architected scalable data pipelines using PySpark and Airflow to process 12M+ behavioral events daily, reducing preprocessing latency by 58% and improving feature reliability consistency by 49% across model training environments. • Designed and trained transformer-based recommendation models, implementing structured feature selection and evaluation workflows that increased personalization accuracy by 44% and reduced model drift occurrence by 41% over quarterly monitoring cycles. • Deployed containerized inference services through Docker and Kubernetes on AWS SageMaker, lowering average API response time by 47% and improving deployment stability metrics by 63% during peak traffic periods. • Established experiment tracking and model version governance using MLflow, strengthening reproducibility controls by 54% and decreasing debugging turnaround time by 48% during production performance investigations. • Implemented real-time monitoring dashboards tracking drift, latency, and cost utilization, enabling proactive optimization strategies that reduced cloud inference expenses by 42% while maintaining performance benchmarks within defined SLA thresholds. ### AI/ML Enginner @ ServiceNow Jan 2021 – Jan 2023 | India • Collaborated with platform engineering teams to convert workflow automation requirements into predictive modeling initiatives, increasing incident resolution accuracy by 51% and decreasing ticket routing errors by 43% across enterprise service modules. • Developed structured ETL frameworks integrating transactional logs and service metadata, improving dataset integrity validation by 57% and reducing manual preprocessing effort by 62% across recurring training pipelines. • Built gradient boosting and neural network classification systems for ticket prioritization, raising precision-recall balance by 48% and shortening average handling time by 45% across high-volume support categories. • Introduced controlled hyperparameter experimentation processes with cross-validation governance, improving model stability variance by 44% and minimizing retraining frequency by 40% across quarterly release cycles. • Delivered REST-based inference endpoints integrated into internal service applications, increasing automation throughput by 59% and decreasing manual assignment dependency by 53% across regional support operations. • Designed performance monitoring frameworks capturing prediction quality, latency distribution, and system utilization metrics, improving operational transparency by 61% and accelerating root-cause identification by 47% during anomaly investigations. ## Education ### Master's Degree in Computer Science New Jersey Institute of Technology ## Contact & Social - LinkedIn: https://linkedin.com/in/asrith-reddy-v - GitHub: https://github.com/Asrith2913/mojo-intake --- Source: https://flows.cv/asrith JSON Resume: https://flows.cv/asrith/resume.json Last updated: 2026-04-18