Jobs at Levio

The DevOps Engineer develops and maintains cloud-based deployment and environment integration capabilities. Responsibilities include automating tools and products, creating Terraform and Ansible deployment scripts, supporting CI/CD pipelines with GitHub Actions, managing AWS infrastructure, implementing DevSecOps and cloud security practices, and enabling environment versioning, application deployment, and testing. The role also contributes to feasibility studies, technology monitoring, software lifecycle activities, and cross-functional technical guidelines.

Designs enterprise security architecture, governance, and risk frameworks for AI and machine learning systems. Responsibilities include establishing secure AI and MLOps architectures, mitigating AI-specific threats, implementing governance aligned with NIST and ISO standards, protecting AI data, ensuring privacy compliance, and advising executives. The role also mentors analysts and guides engineering teams on secure, ethical, and compliant AI deployment.

Lead end-to-end Salesforce Revenue Cloud architecture for complex Quote-to-Revenue implementations. Design pricing, quoting, billing, contract lifecycle, product catalog, data models, integrations, security, and governance across Salesforce, ERP, finance, and downstream systems. Mentor architects and consultants, establish delivery standards, develop reusable frameworks, and grow the Revenue Cloud practice.

Designs, develops, and deploys production-grade AI and generative AI solutions in enterprise cloud environments. Responsibilities include building NLP, LLM, predictive modeling, and intelligent-agent applications; integrating AWS Bedrock and SageMaker; designing APIs and microservices; managing model lifecycle, security, monitoring, and costs; and providing technical leadership, code reviews, and mentorship.

Own and maintain an integration testing framework for large-scale technology projects. Translate manual tests into automated scripts, guide testing strategy, and advise development teams on testable architecture. Manage the integration test backlog while writing, executing, and debugging tests for React applications, APIs, microservices, and AWS environments. Establish quality gates in CI/CD pipelines and support framework adoption across distributed Agile teams.

Designs and leads enterprise AI and generative AI solutions from use-case definition through production. Responsibilities include cloud-native architecture using AWS Bedrock and SageMaker, defining AI standards, selecting platforms and models, partnering with executives, ensuring governance and compliance, and mentoring delivery teams. The role also incorporates responsible AI, risk management, monitoring, lifecycle practices, LLM platforms, RAG, agent systems, and MLOps.

Lead enterprise AI adoption across business and technology teams by developing change strategies, enablement programs, communities of practice, playbooks, governance guidance, and adoption metrics. Embed AI into operational workflows, remove adoption barriers, track ROI and value realization, and partner with technical AI teams. The role requires strong change management, stakeholder engagement, AI literacy, and enterprise technology adoption experience.

Develop and maintain scalable Ruby on Rails applications, REST APIs, backend services, and React interfaces. Collaborate with product, design, and engineering teams; write tested, maintainable code; participate in code reviews and technical design; troubleshoot production issues; use Claude Code and other AI-assisted development tools; and contribute to Agile practices, automation, and continuous improvement.

Design, build, and support production data feeds and integration pipelines using Python, SQL Server, Snowflake, REST APIs, SFTP, Docker, and AWS ECS. Develop incremental-loading workflows, apply Data Vault modeling, troubleshoot end-to-end data pipelines, and implement validation, monitoring, logging, error handling, and retries. Collaborate with technical and business stakeholders while maintaining pipeline and operational documentation.

Build and deploy production-ready generative AI and agentic systems, including RAG, orchestration, tool integrations, evaluation frameworks, and application infrastructure. Partner with enterprise stakeholders to identify high-value AI use cases, design trustworthy solutions within security and governance constraints, and manage systems through production adoption and iteration. Communicate architecture, cost, and delivery trade-offs to technical and non-technical audiences.