Machine Learning Operations Engineer

Qualys · Pune

  • Experience2–4 yrs
  • SalaryNot disclosed
  • Work modehybrid
  • Levelmid
  • Posted11 Sept 2026
Apply for this job

About Qualys

Qualys is hiring in Pune in technology software. This role looks for around 2+ years of experience.

Skills

  • Python
  • PyTorch
  • CI/CD
  • Jenkins
  • Kubernetes
  • model training
  • fine-tuning
  • inference pipelines
  • model monitoring
  • MLflow
  • Kubeflow
  • model versioning
  • Named Entity Recognition
  • Text Classification
  • Natural Language Processing
  • CUDA

The role

A machine learning operations engineer at a product software company builds and deploys NLP models using Generative AI, Kubernetes, and PyTorch. Model monitoring, CI/CD pipelines, and scalable inference operations support reliable production machine learning.

Full job description

We are looking for a highly motivated Machine Learning Operations Engineer with 23 years of experience in building and deploying end-to-end ML products in production environments. The ideal candidate has a strong ML background in Binary/ Multi class Classification, Recommendation Chatbot Applications and deploying training/inference pipelines, with hands-on experience in CI/CD, monitoring, and Kubernetes deployments.

Key Responsibilities:

Design, build, and deploy robust ML pipelines for training, fine-tuning, and inference of models (NLP-focused: NER, Classification).

• Develop and maintain CI/CD workflows for ML pipelines using Jenkins or similar tools, ensuring rapid and safe deployment to production.

• Implement model monitoring and alerting systems to track performance degradation and drift in real-time.

• Collaborate with cross-functional teams to retrain models on trigger events and integrate feedback loops into the ML lifecycle.

• Hands on with Helm deployment of ML Pipelines in Kubernetes cluster and optimize for scalable and resilient operations.

• Use MLflow, Kubeflow, and related tools for experiment tracking, model versioning, and reproducibility.

• Write clean, efficient, and scalable code in Python using frameworks such as PyTorch and CUDA.

• Experience with tuning, optimising LLM Applications performance in production.

Required Skills:

Strong programming experience in Python and PyTorch.

• Hands-on experience with CI/CD pipelines using Jenkins.

• Proficient with Kubernetes for deploying and managing ML workloads.

• Experience with model training, fine-tuning, and inference pipeline development.

• Working knowledge of model monitoring and alerting systems (performance drift, latency, accuracy drop).

• Experience with MLflow, Kubeflow, and model versioning best practices.

• Solid understanding of NER, Text Classification, and common NLP tasks.

• Familiarity with CUDA for training models on GPU.

Good to Have:

Experience with Generative AI systems in production.

• Prior experience with building or deploying applications in Hardwares such as L40S, H100, H200.

• Familiarity with LangChain, LangGraph, LangSmith for building LLM-powered agents and applications.