McAfee - Machine Learning Platform Engineer (4-7 yrs)

About the role :

Develop and maintain scalable pipelines for ML model retraining, containerization, validation, and approval :

- You will be responsible for designing and managing robust pipelines that support the continuous retraining of machine learning models, ensuring they are scalable, secure, and efficient.

- This includes implementing effective containerization strategies for deploying and validating models, as well as managing the approval process to ensure models meet performance standards.

Develop and maintain model training datasets :

- Your work will also involve creating, cleaning, and curating the training datasets needed to power machine learning models, ensuring data quality, consistency, and availability for model development.

Collaborate with Data Scientists to design, validate, deploy, monitor, and maintain production-scale ML models :

- You will actively work alongside Data Scientists to design models that meet business requirements, validate model performance, and deploy them into production environments.

- Additionally, you'll monitor model performance to ensure continuous operation and improvement.

Build and maintain solutions on Databricks, AWS, EKS, and Kubeflow :

- You will be leveraging advanced platforms such as Databricks for big data analytics and model training, AWS for cloud-based solutions, EKS for container orchestration, and Kubeflow for managing machine learning workflows to ensure efficient development and deployment.

Scale up existing pipelines to work with larger datasets and optimize resource usage :

- As the scale of the data increases, you will be responsible for scaling existing pipelines to handle larger volumes of data while also optimizing the use of computational resources to maintain efficiency.

Build and maintain infrastructure as code (IaC) in the cloud, that is resilient and can scale when needed :

- You will use IaC practices to develop resilient cloud infrastructure, allowing for automatic scaling based on demand. This infrastructure will need to be robust, ensuring high availability and performance.

Work with stakeholders across the organization to identify opportunities utilizing big data from many different sources :

- You will collaborate with various stakeholders, including business and technical teams, to identify opportunities where big data from diverse sources can be harnessed to drive innovation and support business goals.

About you :

6-8 years of experience as a software engineer, with expertise in building ML and data pipelines (cybersecurity experience is a plus) :

- You have a solid background in software engineering and a deep understanding of building and maintaining machine learning and data pipelines.

- Experience with cybersecurity is an added benefit, particularly when dealing with sensitive data or security challenges.

Bachelors degree in IT, Software Engineering, or Computer Science is preferred :

- A degree in a relevant field demonstrates a strong academic foundation in the necessary principles for software development and system architecture.

Proficient in Python, Linux shell scripting, and tools like PySpark, Pandas, and NumPy :

- You are skilled in programming with Python, familiar with Linux shell scripting for automation tasks, and have hands-on experience with essential data manipulation tools like PySpark, Pandas, and NumPy, enabling you to handle large datasets and implement complex algorithms efficiently.

Hands-on experience with Databricks, big data management, and core AWS services (EC2, S3, CloudWatch, Lambda, etc.) :

- You have a proven track record of working with Databricks for data analytics and machine learning, managing large datasets, and utilizing AWS services such as EC2 for computing, S3 for storage, CloudWatch for monitoring, and Lambda for serverless computing.

Knowledge of containerization (Kubernetes/EKS) and infrastructure-as-code tools (Terraform or CloudFormation) :

- You are knowledgeable in containerization, particularly with Kubernetes and EKS, and have experience with IaC tools such as Terraform or CloudFormation, enabling you to automate infrastructure management and scaling.

Skilled in debugging computational bottlenecks, troubleshooting, software testing, and optimizing data pipelines :

- You are adept at identifying and resolving issues in data pipelines, particularly performance bottlenecks.

- Your debugging, troubleshooting, and testing skills ensure that the pipelines you develop are robust, reliable, and efficient.

Self-motivated, strong problem-solving skills, and comfortable collaborating remotely with international teams :

- You are a proactive, independent problem-solver who thrives in a collaborative environment.

- Your ability to work effectively with global teams, especially in a remote setup, is key to success in this role.