AI Engineering Manager

Description

Summary: Lead hands-on delivery of AI capabilities within the full SDLC, embedding LLM tooling, shipping production services, and enabling teams with best practices. Highlights: 1. Design and deliver AI-powered solutions embedded into SDLC workflows 2. Build backend services and integrations for LLM-based tooling 3. Implement AI capabilities into CI/CD pipelines and code review processes We are building AI capabilities into the full SDLC and need an **AI Engineering Manage**r to lead hands\-on delivery at scale. You will embed LLM tooling into engineering workflows, ship production services and integrations, and enable teams with guardrails, best practices, and measurable impact—apply now. **Responsibilities** * Design and deliver AI\-powered solutions embedded into SDLC workflows across requirements, development, testing, and CI/CD * Build backend services and integrations that enable LLM\-based tooling within engineering environments * Create prototypes and mature them into production\-ready AI\-assisted automation * Implement AI capabilities into CI/CD pipelines, code review processes, and testing frameworks * Identify SDLC use cases with the highest impact for AI enablement * Define best practices for AI\-assisted development across teams * Support engineering teams hands\-on as they adopt AI tools * Establish guardrails to ensure secure and responsible AI usage * Measure and communicate the impact of AI adoption on cycle time, quality, and productivity **Requirements** * Proven background with 9\+ years of engineering experience in Python (must\-have), with added exposure to Java or Node.js as a plus * Hands\-on experience integrating external APIs, including AI/LLM services * Solid knowledge of CI/CD pipelines along with DevOps ways of working * Expert\-level understanding of microservices architecture plus Docker and Kubernetes * Strong capability to deliver production\-grade services with scalability, monitoring, and logging in mind * Practical experience using LLM APIs and crafting prompts * Deep understanding of end\-to\-end SDLC processes * Demonstrated examples of boosting developer productivity through tooling * Clear ability to drive adoption of new technical practices across engineering teams * Effective communication skills when working with engineers and stakeholders * Flexibility to turn AI capabilities into actionable engineering improvements * Confidence running demos, workshops, and internal technical sessions