MLOps Engineer

6 days ago


Bengaluru, Karnataka, India Connect2Talent Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Job Title : MLOps Engineer

Location : Bangalore / Gurgaon

Experience : 4 to 8 years

Employment Type : Full-time

Notice Period : 15 days

About the Role :

We are seeking an experienced MLOps Engineer who can bridge the gap between Machine Learning and DevOps.

You will be responsible for designing, building, and maintaining scalable pipelines to deploy, monitor, and manage machine learning models in production environments.

This role requires strong programming skills, expertise in MLOps tools, and hands-on experience with cloud platforms.

Key Responsibilities :

- Design, implement, and manage CI/CD pipelines for ML model training, deployment, and monitoring.

- Automate data preprocessing, model training, testing, and production deployment workflows.

- Set up model monitoring, logging, and alerting to ensure performance and reliability.

- Collaborate with data scientists and software engineers to integrate models into production systems.

- Optimize infrastructure for scalability, cost-effectiveness, and high availability.

- Ensure compliance with security, governance, and best practices in ML lifecycle management.

Primary Skills & Qualifications :

- Strong programming skills in Python with a focus on ML/AI workflows.

- Hands-on experience with Docker and Kubernetes for containerization and orchestration.

- Proficiency in MLOps tools such as Kubeflow, MLflow, or similar frameworks.

- Experience with cloud platforms like AWS SageMaker, Azure ML, or equivalent.

- Knowledge of CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) and version control systems (Git).

- Familiarity with model monitoring, model registry, and reproducibility best practices.

Good to Have :

- Experience with Terraform or other Infrastructure-as-Code (IaC) tools.

- Exposure to big data frameworks like Spark or Kafka.

- Understanding of data engineering pipelines and ETL processes.

Why Join Us :

- Opportunity to work on cutting-edge ML infrastructure at scale.

- Collaborative and innovative environment.

- Competitive compensation and growth opportunities.


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