Product Owner

• Design and maintain infrastructure supporting AI/ML workloads and AI-driven development initiatives.
• Integrate and manage Large Language Model (LLM) APIs such as OpenAI, Azure OpenAI, AWS Bedrock, or similar platforms.
• Support prompt engineering practices and enable internal teams to effectively leverage LLM capabilities.
• Deploy, monitor, and optimize AI/ML models in production environments following MLOps best practices.
• Implement monitoring, observability, and governance mechanisms for AI model performance and reliability.
• Support and integrate AI agent frameworks such as LangChain, LangGraph, and AutoGen.
• Design and implement Retrieval-Augmented Generation (RAG) architectures and AI orchestration platforms.
• Work with vector databases and semantic search technologies to enable advanced AI-powered search capabilities.
• Enable the use of AI-powered development tools (e.g., GitHub Copilot or similar) within secure enterprise environments.
• Evaluate and implement responsible AI practices, including bias detection, transparency, explainability, and governance frameworks.
• Ensure AI/ML workloads adhere to security best practices, including secure API integrations, access controls, data privacy, and model protection strategies., The organization is seeking a Technical Product Owner/DevOps Engineer who will also serve as a leader for a development team. This individual will provide product leadership while working closely with engineers to develop internal tools that improve software development and deployment automation., Product Ownership
• Own and manage the product roadmap and backlog for internally developed technical tools.
• Translate business and technical needs into clear user stories, requirements, and acceptance criteria.
• Lead backlog grooming, sprint planning, and other Agile ceremonies.
• Partner closely with developers to prioritize features, enhancements, automation initiatives, and technical improvements.
• Ensure the development team has clear priorities and understands the intended product outcomes.
• Communicate progress, priorities, dependencies, and risks to technical and business stakeholders.

Technical Leadership

• Support the development of an internal Kubernetes controller designed to automate software development and deployment activities.
• Understand Kubernetes concepts sufficiently to guide product decisions related to controller development.

• Work with engineering teams on technical solutions involving:

• Integrations
• Workflow automation
• Containerized platforms
• Kubernetes
• AWS cloud deployments
• Development tools

Help developers identify opportunities to automate software development processes.

Understand how internal developer tools integrate with existing systems and cloud environments.

Provide technically informed direction without necessarily functioning as a hands-on software engineer.

• Bachelor’s degree in Computer Science, Engineering, or related field.
• Proven experience as a Software / DevOps / Platform Engineer delivering enterprise-grade cloud or SaaS solutions across the full SDLC.
• Strong programming skills in Java, Python, and/or Go.
• Hands-on experience with AWS and/or Azure, Kubernetes, Docker, and container orchestration.
• Strong knowledge of microservices architecture, API development (REST/GraphQL), asynchronous processing, and cloud-native design.
• Experience with CI/CD pipelines, Git (GitLab/GitHub), and Infrastructure as Code (e.g., Terraform, Ansible, ArgoCD, Jenkins).
• Experience securing CI/CD and cloud environments (secrets management, container scanning, runtime security, compliance).
• Familiarity with cloud monitoring and observability tools (e.g., Prometheus, Grafana, Datadog, CloudWatch).
• Experience working with internal developer portals (e.g., Backstage), service catalogs, and developer enablement platforms is a strong plus.
• Strong understanding of cloud security, web application security, and information security best practices.
• Experience with AI/ML frameworks and deploying AI models in production environments (MLOps practices).
• Experience working with LLM APIs (e.g., OpenAI, Azure OpenAI, AWS Bedrock) and prompt engineering.
• Knowledge of RAG architectures, AI orchestration platforms, and AI agent frameworks (e.g., LangChain, LangGraph, AutoGen).
• Understanding of vector databases and semantic search technologies.
• Ability to implement responsible AI practices, including bias detection, governance, and AI workload security.
• Experience leveraging AI-powered development tools (e.g., GitHub Copilot).
• Experience working in Agile/Scrum environments.
• Strong problem-solving, communication, and stakeholder management skills.
• Ability to manage projects, define scope, track milestones, and communicate technical concepts to both technical and business audiences., The ideal candidate will be technically proficient, particularly with Kubernetes, cloud platforms, integrations, and automation. They must be able to understand the team’s technology, communicate effectively with developers and business stakeholders, and lead Agile planning activities., * Previous experience supporting a technical product, software platform, or development team.

• Strong understanding of:

• Kubernetes
• Containerized platforms
• AWS cloud deployments
• Systems integrations
• Technical automation

Ability to understand how a Kubernetes controller works and participate meaningfully in discussions regarding its development.

Experience partnering directly with software developers and technical teams.

Experience managing product backlogs and facilitating:

• Backlog grooming
• Sprint planning
• Requirements definition
• Prioritization

Strong knowledge of Agile and Scrum methodologies.

Excellent written and verbal communication skills.

Ability to translate technical concepts into clear product requirements and business outcomes.

Preferred Qualifications

• Python knowledge or development experience.
• Experience implementing or supporting developer tools.
• Experience with cloud-native software development.
• Familiarity with Kubernetes controller or operator development.
• Experience delivering internal platforms or tools that automate software development and deployment processes.