Associate MLOps Engineer @ Qualys

DhirajJagtap

Building scalable ML systems, automating AI workflows, and engineering intelligent pipelines that just work.

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About

Engineering the future of ML systems

I'm an Associate Machine Learning Operations Engineer at Qualys, where I bridge the gap between machine learning models and production systems. My work focuses on building robust, scalable infrastructure that transforms ML experiments into reliable, production-ready solutions.

With a deep interest in LLMs and Agentic AI, I'm constantly exploring new ways to automate and optimize AI workflows. I believe in building systems that are not just functional, but elegant—where every component serves a purpose.

DevOps & CI/CD

Automating deployment pipelines and infrastructure management for seamless delivery.

ML Engineering

Building and optimizing machine learning models for production environments.

Agentic AI

Designing autonomous agent workflows that handle complex, multi-step tasks.

Skills

Tech stack & tools

Python
FastAPI
LangChain
PostgreSQL
Hugging Face
Docker
Kubernetes
MLflow
Linux
Git
LLMOpsAgentic AIVector DatabasesCloud-Native MLOrchestration PipelinesInfrastructure Automation
Projects

Selected work

01

ORBIT — Operational Risk Based Intelligent Telemetry

The Problem

Traditional observability platforms generate overwhelming alert noise, require manual investigation, and lack intelligent signal correlation, leading to slow incident resolution and high operational overhead.

The Solution

Designed an AI-driven observability framework powered by autonomous multi-agent systems that analyze telemetry signals, surface correlations, generate natural-language diagnostics, and progressively enable proactive and self-healing operations.

Agentic AILLMsFastAPIPythonPrometheusGrafanaVector DBKubernetes
02

AI for Metabase — Natural-Language Dashboard Generator

The Problem

Business teams rely heavily on engineers and analysts to create dashboards, resulting in delays and friction between insights and execution.

The Solution

Built an AI-powered workflow that converts natural-language instructions into SQL, generates Metabase cards, and assembles entire dashboards automatically. The system interprets user intent, infers schema context, and handles end-to-end generation from query to dashboard.

PythonFastAPILLMsLangChainMetabase APIPostgreSQL
03

MLflow Platform & Automated Model Migration

The Problem

Model development lacked versioning, governance, and a consistent path from experimentation to production, resulting in delays and unclear model lineage.

The Solution

Implemented an enterprise-grade MLflow platform for experiment tracking, model registry, and lineage. Designed a Jenkins-powered automated migration pipeline that promotes models from development to production based on evaluation metrics and governance checks.

MLflowPythonJenkinsDockerModel RegistryKubernetes
04

Unified Stock Intelligence

The Problem

Retail investors lack a unified, intelligent system that merges technical, fundamental, and news-driven insights for stock decision-making.

The Solution

Created a multi-agent system that analyzes market fundamentals, technical indicators, and real-time news sentiment, then synthesizes insights into predictive signals for better decision-making.

Agentic AIPythonLLMsFastAPIVector SearchPlotting Libraries
05

APIgentMan — Autonomous API Testing CLI

The Problem

Existing API testing tools are heavy, manual, and lack intelligent insight generation for debugging or anomaly detection.

The Solution

Developed a lightweight CLI-based agentic tool that executes API test collections, performs assertions, monitors performance, detects anomalies, and even generates new tests using an LLM. Designed for developers who prefer terminal-first workflows.

PythonAgentic AICLIYAMLLLMsFastAPI
Experience

Career journey

Aug'25 — Present

Associate MLOps Engineer

Qualys

Leading the development of ML infrastructure and deployment pipelines. Building scalable systems for model training, serving, and monitoring across the organization.

  • Reduced model deployment time from days to hours
  • Implemented automated model monitoring and alerting
  • Built centralized feature store for ML teams
Feb'25 — Jul'25

DevOps Engineering Intern

Qualys

Worked on end-to-end ML solutions for natural language processing applications. Developed and deployed models for text classification and entity extraction.

  • Deployed NLP models to production serving millions of requests
  • Optimized model inference latency by 40%
  • Contributed to open-source ML tools
Jun'24 — Jan'25

AI Software Developer Intern

Eduplus Campus

Built backend services and APIs for data-intensive applications. Gained experience in distributed systems and cloud infrastructure.

  • Developed RESTful APIs serving 100K+ daily requests
  • Implemented CI/CD pipelines using GitHub Actions
  • Managed cloud infrastructure on AWS
Contact

Let's build something together

I'm always interested in hearing about new projects and opportunities. Whether you have a question or just want to say hi, feel free to reach out.

Dhiraj Jagtapdhirajdj30@gmail.com
Pune, India