Senior Software Engineer
Indexed description
Essential Functions
- Designs, develops, and maintains self-service Machine Learning Ops tooling, platforms, and automation capabilities that improve the efficiency and scalability of machine learning model delivery
- Implements and supports standardized CI/CD pipelines, model registries, deployment automation, and operational tooling for production AI/ML systems
- Collaborates with Data Scientists, Software Engineers, and Infrastructure teams to reduce operational friction and improve the developer experience associated with model development and deployment workflows
- Designs and implements monitoring, health check, observability, and alerting capabilities for production machine learning and LLM deployments
- Researches and implements scalable infrastructure solutions for LLM deployment including autoscaling, dynamic routing, and cloud-based serving architectures
- Serves in a leadership capacity as an individual contributor for carrying out software development in Python, Java, or other selected languages for new products and/or applications
- May serve as a Lead Software Engineer for complex software development project designs and/or reviews architected modules and software systems supporting new technology or improving capability/performance of existing functionality
- Decomposes functional requirements into well-defined tasks
- Researches fundamental problems and implements algorithm solutions that are appropriate
- Offers peer technical assessments in areas of expertise, new technologies and software designs
- Participates in project leadership and/or program planning including providing technical input to product development plans and concept documents
- Makes substantial contributions toward determination of project goal/objective feasibility and applies good judgment in setting schedules/risk taking
- Mentors and provides guidance to less experienced Software Engineers (1 & 2)
- Contributes to advanced technical research on new technologies
- Offers process improvement suggestions and authors new procedures as appropriate
- Provides reliable solutions to a wide range of difficult problems using sound problem solving techniques
- Supports working hours as part of a rotating schedule to provide on call support of Garmin’s 24/7 operations
- Bachelor’s Degree in Computer Science, Electrical Engineering, Computer Engineering, Software Engineering, Aerospace Engineering, Math, Physics or related field AND a minimum of 5 years relevant experience OR an equivalent combination of education and experience
- Excellent academics (cumulative GPA greater than or equal to 3.0 as a general rule)
- Demonstrated proficiency with designing well architectured software systems and modules that support new technology or improve capability/performance of existing functionality
- Demonstrated competence with researching fundamental problems and implementing appropriate algorithmic solutions
- Demonstrated ability to serve as a lead software engineer for a complex software project
- Ability to decompose functional requirements into well-defined tasks while balancing quality, quantity, and complexity in work output
- Demonstrated capability to offer peer technical assessments in areas of expertise, new technologies and software designs
- Mastered proficiency in writing software in C, C++, C# or Java and relevant experience and/or training in data structures or object-oriented design methodology
- Demonstrated strong and effective verbal, written, and interpersonal communication skills
- Must be positive, detail oriented, organized, team oriented and a driven problem solver, multi-tasker, and self-starter with the ability to prioritize and lead in a fast paced, deadline-driven environment
- Outstanding academics (cumulative GPA greater than or equal to 3.5)
- Experience developing and supporting containerized and distributed systems using technologies such as Docker, Kubernetes, Helm, and cloud-native deployment platforms
- Familiarity with GPU-enabled infrastructure, accelerated compute environments, and scalable inference architectures supporting machine learning and large language model (LLM) workloads
- Experience working with cloud compute and storage platforms such as Microsoft, including deployment automation, networking, and infrastructure management
- Knowledge of ML Ops and DevOps tooling including CI/CD pipelines, infrastructure-as-code, model deployment workflows, observability platforms, and operational monitoring solutions
- Familiarity with modern machine learning frameworks, model serving technologies, and artifact management solutions such as PyTorch, Hugging Face, MLflow, or similar technologies
- Experience developing automation and self-service tooling using Python, REST APIs, scripting frameworks, and cloud based orchestration technologies
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