Job Description

As a Senior Machine Learning Engineer, you will play a pivotal role in driving the development, deployment, and scaling of machine learning models and systems. You will be involved in complex machine learning initiatives, mentor junior and intermediate engineers, and collaborate closely with engineering, product, and data science teams. With your deep expertise in ML engineering and a focus on best practices, you’ll ensure that our solutions are robust, scalable, and aligned with business goals.

Job 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.

Basic Qualifications

• Bachelor's degree and at least 4 years of experience in machine learning, software engineering, or data engineering

• Significant experience programming in one or more of the following: Python, C, C++, Spark, Scala, and/or Java

• 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 (within state and out of state).

Preferred Qualifications

• 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.