Sr Machine Learning Engineer

About the Role

Key Responsibilities

• Research, experiment with, and implement suitable frameworks, tools, and technologies to enable AI/ML decision-making at scale.
• Participate in identifying and assessing opportunities, such as the value of new data sources and analytical techniques, to ensure ongoing competitive advantage.
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
• Accountable for the ownership of design, development, and maintenance of MLOps and GenAI platforms and services.
• Work with junior engineers and peers to provide mentorship and thought leadership.
• Collaborate with partners in Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams.
• Deliver critical milestones for model deployment in Google Cloud Platform (GCP) and AWS cloud environments.
• Develop, adopt, and promote MLOps best practices to the Data Science community.
• Implement infrastructure-as-code using Terraform or CloudFormation to automate deployments.
• Contribute to the development of agentic AI capabilities and support experimentation with LLMs and GenAI frameworks.

Requirements

• Must be authorized to work in the U.S. now and in the future.
• Bachelor's degree in related field and 5+ years of experience.
• Solid understanding of ML lifecycle: model training, deployment, monitoring, and feedback loops.
• Strong application development experience using Python.
• 3+ years of hands-on experience developing with one of the public clouds including tools and techniques to auto scale systems.
• Experience with CI/CD and IAC tools (e.g., terraform, Jenkins, GitHub Actions) and containerization (Docker, Kubernetes).
• Good understanding of Generative AI technologies, frameworks, key LLMs, and architecture patterns.
• Exposure to agentic AI architectures and prompt engineering.
• Good understanding and experience building orchestration frameworks for real-time and batch model services.
• Good understanding of various model development algorithms and types of ML use cases e.g., regression, classification.
• Strong fundamental knowledge of data structures and algorithms.

Nice to Have

• Development experience for WebService API with AWS suite of Tools.
• Familiarity with big data technologies (i.e., Hadoop, Spark, Hive, etc.) and RDBMS.
• Hands-on experience with public cloud GCP, especially Vertex AI, Cloud Run, BigQuery, and GKE.
• Basic understanding of ML frameworks i.e., TensorFlow, Scikit Learn, etc.
• Experience with Agile framework and scrum/Kanban based project management.

Qualifications

• Bachelor's degree in related field.
• 5+ years of experience in relevant roles.

Benefits & Perks

• The listed annualized base pay range is $117,200 - $175,800, with actual pay varying based on performance and competencies.
• Additional rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition.