Senior Software Engineer - ML

Company name confidential

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Posted 24 days ago
Over 400 applicants

2-6 Years

INR 10.2 - 15.1 LPA

Bengaluru

ITES/BPO/Call Center

Job Description

HIRING FOR INFOSYS

Responsibilities:

Design, implement, and maintain scalable and reliable MLOps architectures that support the deployment and monitoring of machine learning models.
Oversee the management of infrastructure for machine learning systems, including cloud services, containerization, and orchestration.
Collaborate with data scientists and machine learning engineers to understand model requirements, optimize deployment models, and ensure seamless integration into production environments.
Establish and optimize CI/CD practices for machine learning projects, enabling efficient testing, building, and deployment of models.
Implement robust monitoring and logging solutions to track model performance, detect anomalies, and ensure the reliability of deployed models.
Ensure that machine learning systems comply with security and privacy regulations, implementing best practices for data protection and model security.
Design and implement solutions for scaling machine learning infrastructure horizontally and optimizing the performance of deployed models.
Establish and maintain version control systems for both code and machine learning artefacts.
Manage a model registry for tracking different versions of machine learning models.
Provide guidance, training, and mentorship to junior members of the MLOps team and other cross-functional teams involved in machine learning projects.
Create and maintain comprehensive documentation for MLOps processes, infrastructure configurations, and best practices.
Develop and implement incident response plans for handling issues related to machine learning model deployment, ensuring minimal downtime.
Learn about new technologies and incorporate them.

Requirements:

Proficiency in programming languages, with a focus on Python and possibly R.
Understanding of machine learning algorithms, model development, and feature engineering.
Experience with popular machine learning frameworks, such as TensorFlow, PyTorch, or scikit-learn.
Strong understanding of DevOps principles and practices, including CI/CD, version control, and automated testing.
Experience with open source MLOps tools, having experience building MLOps infra using open source tools will be a plus.
Experience in building MLOps solutions over Databricks will be a plus.
Experience with containerization tools such as Docker and orchestration systems like Kubernetes for deploying and managing machine learning applications.
Knowledge of infrastructure as code (Terraform, Ansible) and version control for both code and machine learning artefacts.
Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their machine learning services.
Implementation of monitoring and logging systems to track the performance of deployed models and identify issues.
Understanding of security best practices in deploying machine learning models, including data privacy and model robustness.
Effective communication and collaboration skills to work with cross-functional teams, including data scientists, engineers, and business stakeholders.
Experience in mentoring a small team of engineers.
Ability to work in a fast-paced and dynamic environment.