AI Engineering Manager

About the job

Strategic Thinking & Leadership • Partner with business leaders to identify high-impact AI opportunities and translate them into scalable AI/ML solutions. • Define and communicate AI product vision, roadmaps, and measurable success metrics. • Drive AI strategy across predictive analytics, Generative AI, and intelligent automation initiatives. • Establish governance frameworks for Responsible AI, model explainability, fairness, and compliance. • Lead cross-functional AI programs and influence executive stakeholders through compelling insights and presentations. Technical Leadership & Expertise • Architect and oversee end-to-end AI/ML and GenAI systems, including: • Predictive analytics models • Deep learning and neural networks • NLP and computer vision solutions • Retrieval-Augmented Generation (RAG) systems • Agentic AI frameworks and multi-agent orchestration systems • Strong proficiency in Google Cloud Platform (GCP) services for AI/ML (Vertex AI, BigQuery, Dataflow, Cloud Storage) • Deep expertise in machine learning algorithms including ensemble methods, neural networks, regression models, simulation and optimization techniques, NLP, and image processing • Experience building AI systems using TensorFlow, PyTorch, Keras, and Python-based ecosystems • Experience with LLMs, foundation models, prompt engineering, fine-tuning, and evaluation pipelines • Implement scalable MLOps and LLMOps practices including CI/CD for ML, model versioning, monitoring, and automated retraining • Proficiency in Git, Docker, API-based deployments, and scalable cloud AI services • Apply strong software engineering practices within AI systems including testing, modular design, observability, and documentation • Drive research and innovation in advanced AI techniques to enhance enterprise capabilities • Support architectural reviews and ensure best practices across AI systems • Implement Responsible AI principles including governance, model explainability, fairness, and ethical AI compliance Delivery Focus • Own end-to-end AI product delivery in partnership with Product, Engineering, and Data teams. • Ensure production-grade deployment of AI models using containerization (Docker), orchestration, and scalable cloud infrastructure. • Influence investment decisions using measurable impact metrics and ROI analysis. • Establish monitoring frameworks for model drift, performance degradation, and system reliability. Team Development & Community Leadership • Lead and mentor AI engineers and data scientists. • Build AI engineering standards, reusable frameworks, and shared tooling across SSDA. • Promote knowledge sharing through Communities of Practice. • Foster a culture of experimentation, continuous learning, and engineering excellence. • Support talent development in emerging AI domains including GenAI and agent-based systems.

Requirements

• AI Strategy
• Technical Leadership
• Cloud Platforms
• MLOps
• Team Development

Preferred Technologies

• AI Strategy
• Technical Leadership
• Cloud Platforms
• MLOps
• Team Development