Data Software Engineer III - ML Ops

About the role

Northwestern Mutual (NM) has been helping people and businesses achieve financial security for over 169 years. Through a distinctive, whole-picture planning approach including both insurance and investments, we empower people to be financially confident. We combine the expertise of our financial professionals with a personalized digital experience and groundbreaking technology to best serve our clients.

Data is a critical driver of this approach and a cornerstone for how we engage with our customers. To help lead the effort, NM’s Assistant Director, Data Software Engineering – AI/ML Ops is seeking a highly motivated, curious, and passionate software engineers to build and design services, data pipelines, automation, and dashboards for our ML Ops platform and to implement and standardize practices for traditional and generative artificial intelligence.

You will be joining our Data Solutions and Enablement department (DSE) whose mission is to unlock and provide analytical insight on our core customer and client data to better serve our customers, field representative, and business partners. As a part of the team you will collaborate with Data Scientists, Software Engineers, Data Engineers, and Product Owners throughout the organization to help unlock the value of data through predictive analytics, operationalized machine learning, applied AI and generative AI.

Responsibilities

• Building and standardizing services and patterns in Python and Java to enable model deployment, training, inference, and monitoring.
• Building services and automation to streamline and manage the stages of the AI/ML life cycle and model governance.
• Develop reliable data pipelines that transform and aggregate data from NM’s source systems and data platforms.
• Establish and maintain NM’s data science, ML and AI platforms, with a focus on rapid iteration and operational deployment of predictive models, and cost management of workloads.
• Integrating various ML Ops platforms together such as Databricks, AWS Sagemaker, AWS Bedrock.
• Establish a feature store of curated metrics, attributes, and features for ML models.
• Collaborate closely with data scientists, DevOps Engineers, and enterprise infrastructure teams to enable automation and monitoring across the machine learning lifecycle.
• Develop ML model monitoring pipelines for model performance, data quality, and gen AI evaluation, tracing, and metrics.
• Architect and develop scalable data pipelines using advanced programming skills.
• Gather and translate data requirements into technical solutions.
• Optimize sophisticated data integration and transformation processes.
• Enhance existing systems for performance and scalability.
• Mentor junior engineers and oversee CI/CD pipelines.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
• Strong expertise in programming languages for data engineering.
• Experience with data processing frameworks and Kubernetes.
• Proficiency with cloud platforms (e.g., AWS, Azure, Google Cloud) and data visualization tools.
• Understanding of machine learning concepts.
• Expertise in CI/CD processes and version control.
• Expertise in source code management using Git and GitFlow.
• Strong understanding of agile methodologies and experience in an agile development environment.

Skills

• Programming Languages – Advanced proficiency in Python and Java; experience with Spring, Flask, FastMCP, Pandas, Spark, LangGraph, MLflow.
• Databases & Data Platforms – Proficiency with RDBMS (Postgres, SQL Server, MySQL) or big data platforms (Databricks, Spark, Redshift, Snowflake, BigQuery).
• Machine Learning – Familiarity and experience with basic ML algorithms, LLMs, and GenAI/Agentic concepts; exposure to MLflow, Pandas/Numpy/Scikit-learn, PyTorch/TensorFlow, or LlamaIndex/LangChain/LangGraph; or strong mathematical and computer science background.
• Engineering Expertise & Practices – Experience designing and implementing complex data systems, optimizing performance and scalability, and mentoring junior engineers.
• Adaptive Communication – Ability to convey complex technical information to both technical and non-technical audiences.
• Analytical Thinking – Ability to organize and analyze data to identify key issues and improve processes.
• Consulting – Ability to connect with stakeholders to understand and resolve problems using domain knowledge.

Pay

Pay Range – Start: $108,160.00 | End: $162,240.00

Geographic-specific pay structures apply. For eligible locations, compensation ranges may vary:

• Structure 110: $118,960.00 – $178,440.00 USD
• Structure 115: $124,400.00 – $186,600.00 USD

Final salaries are based on skills, experience, market conditions, location, and other factors uncovered during the hiring process.