Software Engineer 2

Job Summary

As a Software Engineer 2 - AI & MLOps , you will design, build, deploy, and support secure, scalable, cloud-native applications and AI-enabled services. You will combine strong software engineering fundamentals with MLOps, automation, observability, and production reliability practices.

Responsibilities

• Develop backend microservices, REST APIs, and AI-enabled solutions using C#, . NET, Python.
• Build and support AIOps workflows for deployment, validation, monitoring, and governance.
• Create automated CI/CD pipelines with quality, security, and production-readiness controls.
• Implement application and AI observability through logs, metrics, traces, alerting.
• Collaborate with architecture, data, product, security, platform, and operations teams to deliver reliable solutions.
• Participate in design and code reviews, testing, sprint planning, production support, and continuous improvement.

Required Qualifications

• Bachelor s degree in Computer Science or a related field, or equivalent experience.
• 3-5 years of professional software engineering experience.
• Strong programming skills in one or more of C#, . NET, or Python.
• Experience with microservices, REST APIs, cloud-native applications, relational databases, data modeling, and query optimization.
• Knowledge of object-oriented design, design patterns, clean code, automated testing, Git, and Agile/Scrum practices.
• Foundational understanding of AI/ML concepts and exposure to model lifecycle or MLOps practices.
• Strong debugging, problem-solving, ownership, communication, and cross-functional collaboration skills.

Preferred Qualifications

• Hands-on experience with Azure, infrastructure as code, or event-driven systems.
• Exposure to Azure AI, Azure Machine Learning, Azure OpenAI, or comparable platforms.
• Familiarity with Application Insights, Azure Monitor, Log Analytics, responsible AI, privacy, and secure development.

What Success Looks Like

You consistently deliver secure, maintainable software; help move AI-enabled solutions from design to production; support reliable model deployment and monitoring; resolve ambiguity and complex issues; and improve platform reliability through automation and collaboration.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.