Lead AI Developer

Description

Summary: Seeking a hands-on Lead AI Developer to drive AI-powered capabilities across the SDLC, integrating AI tools into development workflows and scaling adoption. Highlights: 1. Lead AI Developer with strong technical background 2. Drive AI-powered capabilities across the SDLC 3. Integrate AI tools into development workflows We are seeking a hands\-on **Lead AI Developer** with a strong technical background to drive the adoption and practical implementation of AI\-powered capabilities across the Software Development Lifecycle (SDLC). This role combines engineering execution with enablement, focusing on integrating AI tools into development workflows and scaling their adoption across the entire organization. **Responsibilities** * Design and implement AI\-powered solutions integrated into SDLC workflows (requirements, development, testing, CI/CD) * Build backend services and integrations for LLM\-based tooling within engineering environments * Development of prototypes and production\-ready implementations of AI\-assisted automation * Integrate AI capabilities into CI/CD pipelines, code review processes and testing frameworks * Identify high\-impact SDLC use cases for AI enablement * Establish best practices for AI\-assisted development * Provide hands\-on support to engineering teams adopting AI tools * Define guardrails for secure and responsible AI usage * Measure and report impact of AI adoption (cycle time, quality, productivity) **Requirements** * 5\+ years of engineering experience with Python (must\-have), with additional knowledge of Java or Node.js considered a plus * Background in integrating external APIs, including AI/LLM services * Solid understanding of CI/CD pipelines and DevOps practices * Expertise in microservices architecture, Docker and Kubernetes * Proficiency in implementing production\-grade services (scalability, monitoring, logging) * Practical experience working with LLM APIs and prompt design * Strong understanding of end\-to\-end SDLC processes * Showcase of improving developer productivity through tooling * Capability to drive adoption of new technical practices * Strong communication skills within engineering teams * Flexibility to translate AI capabilities into practical engineering improvements * Comfortable running demos, workshops and internal technical sessions