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, Py Torch, 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.

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, Py Torch, 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.