Key Responsibilities 

AI Architecture & Engineering Leadership

• Design and build scalable, production-ready AI systems aligned with business objectives.
• Lead the architecture, deployment, monitoring, and optimization of AI and machine learning solutions.
• Engineer AI models into robust microservices capable of supporting enterprise-scale workloads.
• Ensure AI solutions perform efficiently across both on-premises and cloud environments.
• Drive best practices in AI engineering, software development, testing, deployment, and monitoring.

Vendor Model Governance & Validation

• Evaluate, validate, and monitor AI and machine learning models delivered by external vendors.
• Assess model performance, fairness, explainability, robustness, and compliance with organizational standards.
• Establish governance frameworks for AI model validation and lifecycle management.
• Support regulatory and governance requirements relating to AI model implementation.
• Monitor vendor-delivered AI solutions and ensure continued effectiveness and reliability.

MLOps & Platform Enablement

• Lead MLOps practices across the AI development lifecycle.
• Design and manage model deployment pipelines, version control processes, and model monitoring frameworks.
• Support automated model retraining, deployment, and performance management processes.
• Implement scalable AI delivery practices leveraging cloud and modern MLOps platforms.
• Drive operational excellence through effective AI lifecycle governance.

AI Team Leadership & Mentoring

• Provide technical leadership, coaching, and mentorship to AI Engineers, Machine Learning Engineers, and Data Scientists.
• Conduct code reviews and technical reviews to ensure engineering quality and consistency.
• Support internal research and development initiatives, proofs of concept, and experimentation activities.
• Develop team capabilities and promote continuous learning within the AI function.
• Guide teams in adopting engineering best practices and responsible AI principles.

Engineering Excellence & Governance

• Establish and maintain high standards across coding, testing, documentation, version control, and model reproducibility.
• Promote explainable AI (XAI), model governance, and responsible AI development practices.
• Support model risk management and regulatory compliance requirements.
• Develop and maintain technical documentation relating to AI solutions, experiments, and deployment frameworks.
• Drive continuous improvement initiatives across AI engineering and data science functions.

Candidate Profile

• Bachelor's Degree in Computer Science, Information Technology, or a related discipline.
• Minimum 5 years of hands-on experience in AI Engineering, Machine Learning Engineering, Data Science, or a related field.
• Previous experience in roles such as Tech Lead – AI/ML, Senior Data Scientist, Senior AI Engineer, Senior Machine Learning Engineer, or similar leadership positions.
• Experience within the Banking or Financial Services sector will be an added advantage.
• Strong technical leadership and people development capabilities.
• Excellent communication skills and ability to engage with technical and business stakeholders.

Technical Expertise

• Advanced proficiency in Python.
• Strong experience working with TensorFlow, PyTorch, Scikit-learn, and modern AI frameworks.
• Hands-on expertise with Large Language Models (LLMs) and Agentic AI architectures.
• Strong MLOps knowledge including MLflow, Kubeflow, SageMaker, Airflow, and related platforms.
• Experience with cloud platforms including Google Cloud Platform (GCP).
• Strong understanding of model governance frameworks, model cards, explainable AI (XAI), and regulatory model validation.
• Knowledge of differential privacy, model monitoring, and enterprise AI governance practices.
• Experience implementing scalable machine learning pipelines and production AI systems.