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Warren, Michigan, United States
Hybrid
Not Available
Job Description
The AI Quality and Productivity team in the Product Validation, Safety and Quality IT org is responsible for empowering Quality and Warranty teams by delivering reliable and secure AI-driven tools that streamline workflows, accelerate decision making, surface actionable insights, and unlock measurable productivity gains across the product lifecycle.
As a Senior Software Engineer, you will be responsible for developing data intensive and AI-enabled software applications that support Product Development engineering teams. You will work with complex data requirements and deliver solutions using industry standard practices across SQL, Databricks, and modern programming languages such as Java and Python. You will design and build software and data solutions based on detailed requirements and system specifications, with a strong focus on data quality, performance, and reliability.
This role offers the opportunity to advise, coach, and mentor other software engineers while shaping how engineering teams access and use data and AI. You will help define patterns and best practices for SQL, Databricks (DBX), and AI/ML integrations within VEQ and play a key role in ongoing modernization and cloud initiatives.
Design, develop, and maintain data driven and AI-enabled applications and services that support Product Development and Quality teams.
Write high-quality, performant SQL (queries, functions, stored procedures) for complex data transformations and modeling across enterprise data platforms (e.g., SQL Server, Oracle, PostgreSQL).
Build and optimize data pipelines and workflows in Databricks (DBX) and related tools to support batch and near real-time data processing.
Develop backend services and APIs in Java and/or Python that integrate data, business rules, and user workflows into robust, reusable components.
Develop enterprise grade applications using Kubernetes/Docker, Quarkus, Java, Angular, PostgreSQL, and other GM approved tools.
Partner with data science and AI teams to productionize AI/ML and LLM based solutions, including feature pipelines, inference integrations, monitoring, and continuous improvement.
Proactively identify and remediate issues related to code quality, patterns, performance, security, and data correctness, using code quality analysis tools and remediation techniques.
Lead or contribute to solution design, including architecture, patterns, and technology choices aligned with GM standards and Statement of Technical Direction.
Apply and champion software engineering best practices, including code reviews, automated testing, branching strategies, CI/CD pipelines (e.g., GitHub Actions), observability, and secure coding practices.
Collaborate with cross-functional teams (product owners, data engineers, architects, business stakeholders) to refine requirements, define acceptance criteria, and deliver incremental value in an Agile/SCRUM environment.
Troubleshoot and resolve production issues across the full stack (data, application, infrastructure), driving root cause analysis, stable fixes, and clear documentation.
Navigate complex corporate processes to remove project roadblocks and keep delivery on track.
Provide guidance and mentoring to fellow software engineers and team members, helping grow skills in SQL, Databricks, cloud, and AI related technologies.
Document software and data solutions and ensure technical documentation meets GM standards and can be leveraged across Product Validation, Safety and Quality teams.
Bachelor’s Degree in Computer Science, Software Engineering, Information Systems, Engineering, or a related field, OR equivalent experience.
6+ years of experience delivering enterprise or full stack software solutions using Java / JEE, Python, and preferably Angular.
3+ years of experience working with complex SQL queries, functions, and stored procedures, including performance tuning and optimization against large datasets.
Experience building or supporting data pipelines, ETL/ELT processes, or datacentric applications on distributed or cloud platforms (e.g., Databricks, Spark, or similar).
3+ years of experience with Kubernetes/Docker, Quarkus, and cloud platforms such as Azure, AWS, or GCP.
Experience working in Agile/SCRUM development methodologies, including backlog refinement, sprint planning, and incremental delivery.
Hands on experience with modern DevOps practices such as Git/GitHub, code reviews, automated builds, automated testing, and CI/CD pipelines (e.g., GitHub Actions).
Willingness and demonstrated ability to learn and apply AI concepts, including working with data and APIs that support AI/ML and LLM based solutions.
Strong problem solving skills with the ability to break down complex technical and data challenges into clear, actionable steps and deliver high quality solutions.
Excellent written and verbal communication skills with the ability to collaborate with both technical and nontechnical stakeholders.
Demonstrated ownership mindset, accountability for quality, and focus on delivering measurable value to internal customers.
Master’s Degree in Computer Science, Engineering, Information Systems, or a related field.
10+ years of experience delivering enterprise grade or global, scalable software applications, including data intensive or analytics focused solutions.
Deep hands on experience with Databricks (Delta tables, notebooks, jobs, workflows) and/or Spark for data engineering, analytics, or AI workloads.
Proven experience migrating applications and complex data workloads to Azure or other cloud platforms, including use of managed data, messaging, and API services.
Experience working with or supporting AI/ML initiatives (e.g., feature engineering, model integration, AI/LLM service integration, monitoring, and ML Ops concepts).
Knowledge of relational and dimensional data modeling, data quality practices, metadata management, and data governance in an enterprise environment.
Experience with observability and reliability practices (logging, metrics, tracing, dashboards, alerting, SRE concepts) for data and application services.
Demonstrated ability to influence technical direction, establish reusable patterns and standards, and mentor less experienced engineers.
Ability to manage multiple initiatives and priorities in a fast-paced environment while maintaining high engineering standards.
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