AI Engineering Manager

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.

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.