Senior Artificial Intelligence (AI) Engineer

Job Summary

The Senior Executive / Senior AI Engineer is a growth-oriented technical lead with the gumption to architect a data-driven future. Driven by intellectual curiosity, the incumbent identifies and resolves complex business problems by transforming raw data into high-value AI assets. They act as a technical catalyst, bridging the gap between sophisticated data architectures and real-time business applications to drive operational excellence.

This role is responsible for the end-to-end AI lifecycle, from optimizing scalable data workflows to training advanced algorithms like computer vision, forecasting, and route optimization. The Senior AI Engineer must possess technical rigor, demonstrating mastery in SQL, Python, and API interfacing while maintaining a strict commitment to data ethics and governance. They ensure all models are high-performing, reproducible, and integrated seamlessly into the organization's production platforms.

Beyond technical execution, the Senior Executive / Senior AI Engineer is a persuasive data storyteller who translates complex model outputs into compelling narratives for stakeholder buy-in. With strong analytical and critical thinking, they influence leadership and secure commitment for AI-driven initiatives. By fostering a collaborative team environment and championing data quality, they lead the organization's transition toward a sophisticated, AI-centric operational model.

Job Responsibilities/Key Tasks

Operations and Data Engineering Excellence

. Design and optimize scalable data workflows with subject matter expertise in data loading patterns and architecture.

. Develop and maintain robust data pipelines supporting both real-time and batch processing to power the Supply Chain Control Tower.

. Extract and integrate data from structured, semi-structured, and unstructured sources. Develop API interfaces between the data layer and application layer for real-time scalability.

. Leverage advanced SQL and partitioning strategies to ensure efficient data transformation and high-performance analytics.

. Champion data quality by implementing validation, lineage tracking, and compliance standards (Data Ethics).

Model Development and AI Deployment

. Train and deploy advanced models, including Computer Vision, Forecasting, and Route Optimization, suitable for specific business use cases.

. Implement data modelling best practices (schema design, indexing) specifically tailored for AI/ML applications to optimize performance.

. Work collaboratively to ensure data readiness, rigorous feature engineering, and the reproducibility of all experiments.

. Perform model comparisons to draw inferences on variable importance select and interpret models based on pre-defined criteria (accuracy, latency, and business impact).

. Conduct rigorous testing of models in real-time business conditions prior to full-scale deployment, documenting all techniques and assumptions.

Strategic Insights and Value Delivery

. Conduct deep-dive analysis to determine the best analytical path for solving complex operational problems.

. Evaluate and implement autonomous monitoring solutions to scale human oversight of deployed AI models.

. Create compelling, logically structured presentations that use data storytelling to translate model results into actionable business commitments.

. Contribute to the creation of leading-edge resources, including internal playbooks, guides, and technical blog posts to enable end-user capability across the organization.

Others

. Undertake any assigned projects/duties directed by Management (if any)