Machine Learning Operations Engineer

Qualys; Pune District; Hybrid; INR 900,000 - 1,500,000; Full time

Job summary

Qualys is seeking a driven Machine Learning Operations Engineer to design, deploy and monitor end-to-end ML products in production. You will own training, inference pipelines, and model lifecycle with NLP-centric tasks including NER and Classification.

The role requires hands-on experience with CI/CD (Jenkins), Kubernetes, and tools like MLflow and Kubeflow, along with Python and PyTorch expertise. Deep learning workloads on GPUs are common in this position.

Qualifications

• Proficient in Python and PyTorch.
• Experience building CI/CD pipelines (Jenkins) for ML models.
• Kubernetes deployment and management for ML workloads.
• Experience with MLflow, Kubeflow, and model versioning.

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, enabling rapid production deployments.
• Implement model monitoring and alerting to track performance drift in real time.
• Collaborate with cross-functional teams to retrain models on events and feedback loops.
• Hand-on Helm deployment of ML pipelines in Kubernetes and optimize for scalable operations.
• Use MLflow, Kubeflow, and related tools for experiment tracking and reproducibility.
• Write clean, scalable Python code using PyTorch and CUDA.
• Tune and optimise LLM applications in production.

Skills

Python

PyTorch

CI/CD pipelines

Kubernetes

NLP tasks

CUDA

Tools

Jenkins

MLflow

Kubeflow

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.