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. Minimum Requirements
• Bachelor’s Degree in a related field (Data Science, Machine Learning, Computer Science, Statistics, Applied Mathematics, IT, or equivalent).
• 5 to 8 years of experience applying analytical methods and AI/ML solutions in enterprise environments.
• 5 to 8 years of experience using Python-based AI/ML technologies.
• Experience leading AI or Data Science teams.
• Experience acting as a senior technical lead facilitating solution trade-offs and architectural decisions.
• Experience using Cloud AI Platforms (GCP preferred).
• Hands-on experience with Generative AI technologies and enterprise AI deployment. Preferred Requirements
• Master’s or PhD in Data Science, Machine Learning, Statistics, Applied Mathematics, or Computer Science.
• Experience managing and growing high-performing AI teams.
• Expert-level knowledge in advanced predictive analytics and AI techniques (Genetic Algorithms, Ensemble Learning, Neural Networks, NLP, Simulation, Design of Experiments).
• Strong working knowledge of GCP and enterprise AI architecture patterns.
• Expertise in open-source technologies such as Python, R, Spark, SQL.
• Experience building enterprise-grade GenAI and agent-based AI solutions.