Kowshik Mundlamuri

Northlake,USA

Summary

Senior AIOps and ML Platform Engineer with approximately 10 years of experience building intelligent automation, observability, machinelearning, and cloud-platform solutions across financial services, enterprise technology, and healthcare. Built and operated a GCP-native platform supporting 100+ production models, approximately 30,000 requests per minute, sub-100-millisecond inference latency, and 99.5% availability. Experienced in applying anomaly detection, operational analytics, model monitoring, distributed tracing, alerting, and workflow automation to improve production reliability. Hands-on expertise with Kubernetes, GKE, AKS, Terraform, Argo CD, GitLab CI, Kubeflow, Datadog, Grafana, Evidently AI, Amazon CloudWatch, AWS X-Ray, Ray Serve, NVIDIA Triton, and multi-cloud infrastructure.

Overview

years of professional experience

Work History

Senior ML Platform Engineer

MX TECHNOLOGIES

04.2023 - Current

Engineered and operated a GCP-native ML platform supporting 100+ production models, approximately 30,000 requests per minute, sub-100-millisecond inference latency, and 99.5% service availability across Vertex AI and GKE.
Built an operational monitoring framework using Datadog and Evidently AI to track model performance, production health, data-quality changes, and model drift across deployed services.
Automated Kubeflow workflows to initiate model validation and retraining when production performance declined because of data drift or model degradation, reducing manual monitoring and intervention.
Developed dashboards and alerting capabilities that gave engineering teams visibility into model behavior, application performance, deployment health, and operational exceptions.
Deployed GPU-backed inference services using Ray Serve and NVIDIA Triton on GKE, implementing health checks, node affinity, pod disruption budgets, and resilient deployment patterns to maintain availability under high traffic.
Automated infrastructure provisioning, environment configuration, model deployment, and application releases using Terraform, Argo CD, GitLab CI, and Cloud Build, reducing deployment lead time by 60%.
Built a real-time fraud-intelligence platform processing more than one million financial transactions per day and incorporated behavioral analytics that reduced false-positive detections by 25%.
Designed an enterprise generative-AI assistant for merchant-risk, transaction-investigation, and financial-operations teams, enabling analysts to retrieve merchant profiles, transaction activity, policies, case history, and operational procedures.
Developed agentic workflows that retrieved financial and operational information, invoked internal APIs, summarized investigation findings, recommended next actions, and used validation, approval gates, and audit logging for controlled execution.
Built Pub/Sub, Dataflow, BigQuery, PySpark, Cloud Composer, and Dataproc pipelines that increased data-processing throughput by three times.

Senior Machine Learning Engineer

01.2020 - 03.2023

Designed an intelligent conversational-automation platform using BERT-based intent classification and entity recognition to interpret operational requests and connect users with Oracle, Salesforce, Jira, and enterprise data catalogs.
Implemented asynchronous processing with Celery and Redis to control database workload, improve responsiveness, and increase reliability for high-volume enterprise queries and action workflows.
Created operational dashboards and performance metrics using Django, Grafana, and D3.js to provide visibility into application usage, system behavior, and performance trends.
Built deep-learning classification and named-entity-recognition models to automate high-volume Salesforce service requests across financial business processes.
Containerized and deployed ML applications on AKS and automated releases through Azure DevOps and Azure Pipelines.
Developed a document-intelligence pipeline using Cascade Mask R-CNN, AllenNLP, and TabNet to detect tables and extract information from thousands of financial documents each day.

Machine Learning Engineer

CHANGE HEALTHCARE

06.2018 - 01.2020

Built event-driven conversational-AI and healthcare-operations services using Amazon Lex, Amazon Connect, Lambda, API Gateway, Step Functions, Kinesis, and DynamoDB.
Used Amazon CloudWatch to monitor application health, Lambda execution, service errors, and operational metrics, and implemented AWS X-Ray distributed tracing to diagnose performance issues across serverless services.
Orchestrated multi-step operational processes with Step Functions, improving workflow visibility, error handling, and recovery for appointment, prescription, patient-record, claims, and document-processing services.
Provisioned repeatable infrastructure with CloudFormation and automated releases with CodePipeline, CodeBuild, and CodeDeploy.

Data Scientist / Machine Learning Engineer

INNOVA SOLUTIONS

06.2016 - 06.2018

Developed an Intelligent Data Science Platform providing anomaly detection, conversational AI, sentiment analysis, OCR, object detection, classification, and named-entity-recognition capabilities.
Built anomaly-detection capabilities for financial datasets and real-time sentiment and anomaly-monitoring pipelines covering social-media data for more than 500 companies.
Implemented human-in-the-loop routing for complex or unresolved requests and exposed ML capabilities through Django REST APIs.
Automated application and model delivery using Jenkins and GitLab CI.

Education

Bachelor of Technology - Electronics and Communication Engineering

Shiv Nadar University

India

Skills

AIOps and Observability: Datadog, Grafana, Evidently AI, Amazon CloudWatch, AWS X-Ray, Google Cloud Logging, Application Monitoring, Model Monitoring, Drift Detection, Anomaly Detection, Distributed Tracing, Operational Dashboards, Alerting, Health Checks, Performance Monitoring

Cloud and Infrastructure: Google Cloud Platform, Amazon Web Services, Microsoft Azure, Kubernetes, Google Kubernetes Engine, Azure Kubernetes Service, Docker, Singularity, Terraform, Argo CD, AWS CloudFormation, Linux

Automation and DevOps: GitLab CI, Google Cloud Build, Azure DevOps, Azure Pipelines, Jenkins, AWS CodePipeline, CodeBuild, CodeDeploy, Infrastructure as Code, GitOps, CI/CD, Automated Deployment, Workflow Orchestration

MLOps and AI Platforms: Kubeflow Pipelines, MLflow, Vertex AI, Ray Serve, NVIDIA Triton Inference Server, Automated Retraining, Experiment Tracking, Model Deployment, Model Performance Monitoring, GPU Workloads

Generative AI and Intelligent Automation: LangChain, LlamaIndex, LangServe, Retrieval-Augmented Generation, Qdrant, OpenAI APIs, Hugging Face, Open-Source LLMs, Agentic Workflows, Tool and API Integration, Human-in-the-Loop Workflows

Programming and Frameworks: Python, Go, Ruby, SQL, JavaScript, PyTorch, TensorFlow, Keras, FastAPI, Django, Django REST Framework, Flask, Ruby on Rails

Data Engineering and Processing: PySpark, BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Airflow, Celery, Redis, Hadoop, Pandas, NumPy, Dask, Polars, DuckDB

Personal Information

Title: Senior AIOPS & ML PLATFORM ENGINEER

Technical Skills

Datadog, Grafana, Evidently AI, Amazon CloudWatch, AWS X-Ray, Google Cloud Logging, application monitoring, model monitoring, drift detection, anomaly detection, distributed tracing, dashboards, alerting, health checks, GCP, AWS, Azure, Kubernetes, GKE, AKS, Docker, Singularity, Terraform, Argo CD, CloudFormation, Linux, GitLab CI, Cloud Build, Azure DevOps, Jenkins, AWS CodePipeline, CodeBuild, CodeDeploy, Infrastructure as Code, GitOps, CI/CD, workflow orchestration, Kubeflow Pipelines, MLflow, Vertex AI, Ray Serve, NVIDIA Triton, automated retraining, experiment tracking, model deployment, GPU workloads, LangChain, LlamaIndex, LangServe, RAG, Qdrant, OpenAI APIs, Hugging Face, agentic workflows, tool/API integration, human-in-the-loop workflows, Python, Go, Ruby, SQL, PyTorch, TensorFlow, Keras, FastAPI, Django, Flask, PySpark, BigQuery, Dataflow, Dataproc, Pub/Sub, Airflow, Celery, Redis

Timeline