AI Engineering Manager

Anju Software AI Engineering Manager The AI Engineering Manager leads a cross-functional team (Development & QA) and owns technical architecture, delivery outcomes, AI-enabled system design and driving an AI first culture among your team. This role is both strategic and hands-on. It requires strong expertise in modern AI frameworks, applied AI architectures, and scalable system design. The manager is accountable for delivering stable, secure, AI-powered capabilities — not just managing execution. Core Responsibilities Technical Leadership & Architecture •Own product architecture, including AI/LLM-integrated components •Design scalable, secure, and maintainable systems •Lead decisions involving: oLLM integration (OpenAI, Azure OpenAI, etc.) oRAG architectures and vector databases oAI orchestration frameworks (LangChain, Semantic Kernel, LlamaIndex) •Ensure system reliability, observability, and cost efficiency •Establish AI governance and safe usage practices AI Framework & Methodology Expertise •Apply modern AI methodologies, including: oRetrieval-Augmented Generation (RAG) oPrompt engineering and evaluation oAI output validation and guardrails oHuman-in-the-loop workflows oModel monitoring and performance evaluation •Drive experimentation, A/B testing, and telemetry-based decision making Team Leadership •Lead and mentor developers and QA engineers •Raise AI literacy across the team •Establish AI-native coding and review standards •Balance speed, quality, and architectural integrity Delivery & Outcome Ownership •Own predictable, high-quality releases •Ensure AI features are measurable, validated, and production-ready •Act as technical escalation point •Drive cross-team alignment with Product, Data, and DevOps AI-Driven Engineering Excellence •Leverage AI-assisted development tools to improve velocity •Optimize AI cost-performance tradeoffs •Embed automation into testing and CI/CD pipelines Qualifications 7+ years software engineering experience, 2+ years in technica