Devops Engineer Lead

Description -

Design, develop, and operate secure cloud platform services and software that meet business requirements.
•Create GitHub Actions and workflows for standardize CI/CD pipelines within UHG.•And ensure that pipelines are following best practices, documented and delivered in a scalable reusable manner.•Demonstrate innovation, streamline processes, and reduce technical debt by standardizing patterns.•Automate repetitive tasks (TOIL), monitor applications, simplify work practices, define metrics, and ensure the operational quality of applications.•Manage security controls at the platform level to enable secure, efficient, and policy-compliant operations.•Collaborate with multiple partner teams to communicate information security guidance and standards and reduce information security risks.AI User Responsibilities•Uses GitHub Copilot to speed up the creation of boilerplate code•Uses M365 Copilot to recap meetings and draft emails•Uses a chat-based AI tool to debug code•Uses GenAI to create sample test data or test cases•Uses StoryCraft to streamline the creation of user stories

AI Expectations:
Will utilize AI tools and applications to perform tasks, make decisions and enhance their work and overall cloud migration velocity. Expected to use AI Powered software for data analysis, coding, and overall productivity.

Qualifications -

Preferred Qualifications
•Proven excellent verbal, written and interpersonal communication skills.•Experience with containers and orchestration platforms, such as Kubernetes.•Experience with Cloud Computing (AWS/Azure/GCP), DevOps tools and automation using Python or any other scripting language.•Evidence-based approach to decision-making and problem-solving.•Design mindset with the ability to construct scalable, distributed services.•Proficient in algorithms, data structures, object-oriented programming, and system design•Knowledge of API design and lifecycle management (REST, etc.).•Expertise in data storage, caching, and optimization (SQL and NoSQL databases)•Familiarity with inter-service messaging and stream discovery (SQS, Pub/Sub Kafka, etc.).•Proficiency in instrumentation, logging, and tracing tools (Prometheus, CloudWatch, Stack Driver, Azure Monitor, etc.).

AI Expectation :
Good understanding of LLMs and generative modelsStrong conceptual grasp of what LLM's are how they work and different types of generative models(GPT, DALL-E, diffusion models)Prompt EngineeringHow to effectively create prompts to get desired output from generative models.Working with Generative AI API's and FrameworksHands on experience with API's like OpenAI, Google Gemini and Hugging faceFamiliarity with frameworks like Lang Chain, AutoGen and Microsoft Copilot Studio etc.Proficiency with AI-Assisted Development ToolsExperience in using Tools like GitHub Copilot, Cursor, Windsurf/Amazon Code Whisperer etc.