We are seeking a highly skilled Machine Learning Engineer with expertise in Automation Tools and Natural Language Processing (NLP) to join our dynamic team. In this role, you will work on designing, building, and deploying ML models and automation pipelines that enhance the efficiency and intelligence of our systems. You will leverage cutting-edge technologies such as Amazon Bedrock, SageMaker Studio, and other NLP tools to develop robust, scalable solutions for complex business challenges and enhance business processes across various departments, including Customer Operations, Revenue Growth, and Online Marketing. The ideal candidate will have hands-on experience working with both structured and unstructured data, leveraging automation tools to streamline workflows and improve business outcomes.
As a part of our team, you will collaborate with cross-functional teams to integrate ML solutions into real-world applications, with a focus on automation, data-driven decision-making, and NLP capabilities.
Key Responsibilities:
-
Design and Develop Machine Learning Models: Build, train, and optimize ML models for a variety of applications, including natural language processing, predictive analytics, and recommendation systems.
-
Automation of ML Pipelines: Create and implement automated workflows for data collection, preprocessing, model training, validation, and deployment using tools such as AWS SageMaker, Amazon Bedrock, Apache Airflow, and other DevOps or CI/CD tools.
-
Leverage NLP Tools: Utilize Amazon Bedrock, SageMaker Studio, and other NLP frameworks to build state-of-the-art models for text analysis, sentiment analysis, chatbots, language understanding, and content generation.
-
Automation Tools Expertise: Create and manage automated workflows and pipelines Streamline data ingestion, processing, model training, and deployment pipelines to increase efficiency and reduce manual effort.
-
Data Integration and Pipeline Automation: Automate the handling of structured data (e.g., from databases and APIs) and unstructured data (e.g., from customer reviews, emails, and social media) to drive intelligent insights for decision-making. Use Amazon Bedrock and SageMaker Studio for seamless integration
-
Collaborate with Data Engineers: Work with data engineers to ensure seamless integration of machine learning models into data pipelines, ensuring data is prepared and processed correctly for training and inference.
-
Deploy and Monitor Models: Implement best practices for deploying and maintaining machine learning models in production, ensuring scalability, performance, and reliability. Utilize AWS SageMaker and Bedrock for model management and deployment.
-
Optimization and Troubleshooting: Continuously improve models by troubleshooting issues and optimizing performance through fine-tuning, hyperparameter tuning, and advanced techniques like transfer learning and model compression.
-
Optimize Marketing Campaigns: Use machine learning models to automate and optimize marketing campaigns by analyzing customer data, improving customer targeting, segmentation, and personalizing marketing messages.
-
Data and Model Monitoring: Set up automated monitoring tools to track the performance of data pipelines and models, providing real-time insights and alerts for issues such as data drift or model degradation.
-
Documentation and Reporting: Document automated workflows, data pipelines, model performance, and results. Provide clear, actionable reports to stakeholders and ensure that models are continuously aligned with business goals
-
Model Monitoring & Optimization: Continuously monitor model performance, troubleshoot issues, and implement improvements to ensure that deployed models meet business objectives, and are scalable and efficient.
-
Data Analysis and Insights: Analyze large datasets to uncover patterns, trends, and actionable insights that drive strategic decisions for customer operations and revenue growth.
-
Collaborate Across Departments: Partner with Customer Operations, Revenue Growth, Online Marketing, and other departments to understand their challenges and develop tailored machine learning solutions to meet business needs.
-
Stay Up-to-date with Industry Trends: Keep up with the latest developments in machine learning, AI, and NLP. Integrate new techniques, tools, and frameworks as appropriate.
-
Document and Report Findings: Maintain clear documentation of models, processes, and code. Provide regular reports to stakeholders on model performance, automation efficiency, and overall system improvements.
-
Educational Background: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
-
Machine Learning Expertise: Strong experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
-
NLP Tools and Frameworks: Hands-on experience with NLP techniques and platforms, particularly Amazon Bedrock, SageMaker Studio, Hugging Face, or similar tools for language modeling and understanding.
-
Automation Tools: Familiarity with automation platforms like AWS SageMaker, Apache Airflow, Jenkins, or similar CI/CD automation tools for ML workflows.
-
Programming Languages: Proficiency in Python, Java, or R. Strong experience with data manipulation libraries like Pandas, NumPy, and SciPy.
-
Cloud Experience: Expertise in deploying and managing machine learning models on cloud platforms, particularly AWS (Amazon Web Services), with experience in SageMaker, Lambda, EC2, and S3.
-
Data Engineering Knowledge: Experience with data wrangling, cleaning, and preparing large datasets. Familiarity with SQL and NoSQL databases.
-
Problem Solving: Strong analytical and problem-solving skills with the ability to address complex challenges in model development, deployment, and optimization.
Preferred Qualifications:
-
Experience with Large-Scale Distributed Systems: Experience working on large-scale ML models and distributed computing environments.
-
Knowledge of Model Interpretability: Experience with tools and techniques for model explainability, interpretability, and ethical considerations in AI.
-
Experience with Deep Learning: Proficiency in building deep learning models for computer vision or speech recognition is a plus.
-
Communication Skills: Excellent written and verbal communication skills to interact with stakeholders and document technical concepts effectively.
Western Digital thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.
Western Digital is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [email protected] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
