Data Software Engineer III - ML Ops
About The Position
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.
Requirements
- We work in Python and Java, leveraging Spring, Flask, FastMCP, FastMCP, Pandas, Spark, LangGraph and ML Flow
- We leverage AWS and Databricks often and deploy software and AI/ML solutions CI/CD first. We aspire to automate and standardize everything.
- We expect proficiency with databases and SQL from RDBMS (Postgres, SQL Server, MySql etc.) or big data platforms (Databricks, Spark, Redshift, Snowflake, Big Query etc).
- We expect familiarity and experience with basic ML algorithms, LLMs, and GenAI/Agentic concepts.
- We expect an understanding of basic tools and libraries common to data science, AI and ML. e.g. ML Flow, Pandas/Numpy/Sklearn, PyTorch/TensorFlow, or LlamaIndex/LangChain/LangGraph OR a strong mathematical and computer science background.
- 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 CI/CD processes and tools (e.g., Jenkins, GitLab CI/CD, CircleCI) and experience with artifact repositories (e.g., Nexus, Artifactory)
- Strong understanding of agile methodologies and experience in an agile development environment
- Adaptive Communication (NM) - Formulates strategies to be used to convey complex information about services, products, systems, or processes to targeted audiences; communicates and liaises between technical and non-technical audiences.
- Analytical Thinking (NM) - Organizes and compares various aspects of a situation to comprehend and identify key or underlying complex issues through the use of quantitative data and analysis; leverages strong business acumen, problem solving, and interpersonal skills to think critically about situations from multiple perspectives and consistently seeks ways to improve processes.
- Consulting (NM) - Connects with stakeholders to understand and gain specific information to help resolve customer problems in a given domain. Communicates effectively intent to customers, solicits customer requirements, utilizes domain knowledge and collaborates with the right stakeholders.
- Databases & Data Platforms (NM) - Utilizes knowledge of databases to access, manage, and update information, typically containing aggregations of data records or files; includes understanding of different types of databases.
- Engineering Expertise & Practices (NM) - Applies specialized experiences in different facets of engineering, including data, applications, cyber, systems, operations, product, security, and testing, along with technical aptitude to adapt new expertise as they become relevant through an understanding of underlying engineering principles.
- Machine Learning (NM) - Applies understanding of and/or computes large data structures and sets using quantitative analysis methods, while building out data pipelines and statistics.
- Programming Languages (NM) - Demonstrates proficiency in one or more programming languages to execute activities, tasks, practices, and deliverables associated with writing and modifying programs and scripts that comprise an application system; designs, codes, tests, and installs complex computer programs and maintains detailed documentation of programming tasks.
Nice To Haves
- We are passionate about continuous learning and problem solving. Curiosity is expected, welcome, and rewarded.
- We collaborate and work creatively every day.
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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
