Senior Software Engineer - Platform

General Motors•Austin, MI

1d•Hybrid

About The Position

General Motors is seeking a Senior Software Engineer to support, design, and improve delivery of enterprise applications, integrations, and intelligent platform capabilities across the Global Physical Security and Medical portfolio. This role focuses on modernizing mission-critical solutions across on-prem, cloud, SaaS, and hybrid environments, strengthening system reliability, enabling scalable integrations, and driving technical execution across a complex operational landscape. The Senior Software Engineer will also help introduce AI-enabled capabilities in practical, secure, and responsible ways, supporting automation, advanced insights, and intelligent workflows that improve both service delivery and business decision-making.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field
5+ years of professional software engineering, systems engineering, or platform support experience
Strong experience with scripting and automation using Bash, Python, Java or similar languages
Hands-on experience with Terraform, GitHub, Linux, Windows Server, and SSO/SAML
Experience with cloud platforms such as AWS and/or Azure
Experience building and supporting CI/CD pipelines, deployment automation, testing strategies, and environment promotion
Experience with Databricks Asset Bundles, GitHub Actions, or similar modern delivery tooling
Experience deploying and supporting containerized applications using Docker, Kubernetes, or similar technologies
Experience with SQL and NoSQL databases such as PostgreSQL, MongoDB, and Redis
Strong Linux administration and networking fundamentals
Solid understanding of security practices including RBAC, secrets management, TLS, patching, and audit controls
Demonstrated ability to evaluate, design, and implement deployment architectures for complex data and application platforms
Excellent troubleshooting, communication, and documentation skills

Nice To Haves

Experience with BrowserStack
Experience with OTEL, DataDog, and modern observability practices
Practical experience with tools such as Databricks Genie, Glean, Cursor, GitHub Copilot, or similar AI-enabled engineering tools
Familiarity with MCP-style integrations and AI tools that connect to enterprise systems and services
Experience applying AI and LLM capabilities to operational workflows, analytics, and decision support
Ability to uncover patterns and insights in structured and semi-structured data and translate them into business actions
Experience building real-time alerts, operational signals, predictive indicators, or intelligent automation workflows
Strong ability to frame AI-assisted analytical problems, evaluate model output quality, and apply responsible AI practices across governance, security, privacy, and quality

Responsibilities

Build, maintain, and support COTS applications on Unix and Windows platforms that are essential to business operations
Deploy applications and infrastructure as code using Terraform and similar tools
Lead and support application migrations to SaaS and cloud-based platforms
Create, maintain, and improve automation and operational scripts for deployments, backups, monitoring, rollbacks, and routine maintenance
Build and support CI/CD pipelines across applications, integrations, and platform services
Monitor platform health and data pipelines, including alerting, capacity planning, performance tuning, and operational readiness
Plan and execute backups, disaster recovery testing, and restore procedures for supported applications
Enforce security best practices including secrets management, RBAC, patching, vulnerability remediation, SSO/SAML, and audit logging
Troubleshoot incidents across the application and integration stack, perform root cause analysis, and produce post-incident documentation
Maintain runbooks, playbooks, technical documentation, and regular housekeeping procedures
Drive improvements in system reliability, maintainability, observability, and supportability
Apply modern engineering practices including Agile, DevSecOps, CI/CD, automated testing, and release governance
Identify and implement AI and intelligent automation opportunities that improve efficiency, user experience, and business outcomes
Develop agentic AI solutions that streamline business processes and enable practical, secure, and responsible use of AI
Evaluate new tools and technologies through proofs of concept
Mentor junior engineers and collaborate cross-functionally to deliver scalable platform and integration solutions