Senior Machine Learning Engineer - ML Ops

About the Role

Key Responsibilities

• Develops software that processes, stores and serves data and machine learning models for use by others.
• Develops large scale data structures and pipelines to organize, collect and standardize data that helps generate insights and intelligence to support business needs.
• Writes ETL (Extract / Transform / Load) or ELT processes, designs data stores and develops tools for real-time and offline analytic processing on premise or on cloud infrastructure.
• Develops and maintains optimal data pipelines into the ML and advanced analytics platform, including design of data flows, procedures, and schedules.
• Ensures that optimal data pipelines are scalable, repeatable and secure.
• Troubleshoots software and processes for data consistency and integrity.
• Integrates data from a variety of sources, assuring that they adhere to data quality and accessibility standards.
• Anticipates and prevents problems and roadblocks before they occur.
• Interacts with internal and external peers and managers to exchange complex information related to areas of specialization.
• Collaborates with AI/ML scientists and data scientists to prepare data for model development, and to deploy models to production.

Requirements

• Bachelor's degree and at least 4 years of experience in machine learning, software engineering, or data engineering.
• Deep knowledge of SQL.
• Significant experience programming in one or more of the following: Python, C, C++, Spark, Scala, and/or Java.
• Experience establishing and maintaining key relationships with internal and external stakeholders within a matrix organization.
• Experience diagnosing, isolating, and resolving complex business issues and recommending strategies.
• Experience presenting to all levels of an organization.
• At least 2 years of experience contributing to financial decisions in the workplace.
• At least 2 years of direct leadership, indirect leadership, and/or cross-functional team leadership.
• Willing to travel up to 10% of the time for business purposes.

Nice to Have

• Graduate degree in a technical discipline and at least 2 years of experience in machine learning, software engineering, or data engineering.
• Strong experience designing and implementing monitoring and alerting systems for cloud-based applications, including log management and analysis tools (e.g., ELK stack, Splunk).
• Experience with cloud platforms (AWS, Azure, GCP) and their AI/ML services, as well as deploying ML models at scale in production using open-source tools (e.g., Kubeflow, Seldon).
• Proficiency in CI/CD practices for AI/ML model development and deployment, with experience using tools such as Azure DevOps, Tekton, or GitHub Actions.
• Experience with Infrastructure as Code (IaC) tools, particularly Terraform.
• Familiarity with DAG-based workflow orchestration systems (e.g., Airflow, Prefect) and data processing pipelines using Apache Spark or Databricks.
• Experience working with ML registries (e.g., MLFlow) and deploying event-driven or reactive ML applications.
• Strong background in deploying and maintaining ML systems for both batch and streaming data.
• Expertise in troubleshooting distributed systems, optimizing performance, and reducing costs in cloud environments.
• Proficient in writing and deploying production-grade Python applications and libraries.
• Experience with REST API development and configuring Kubernetes in multi-tenant environments.

Qualifications

• Bachelor's degree in a relevant field
• At least 4 years of experience in machine learning, software engineering, or data engineering

Benefits & Perks

• Salary Range: $127,500 - $204,000 / Salaried