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Job Description & How to Apply Below
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, Big Query, 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 Tensor Flow, 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, Big Query, 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 Tensor Flow, 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…
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