Software Engineer Lead (Python/ DataLake)

Key Responsibilities

• End-to-End System Ownership - Lead the development and integration of a comprehensive AI-driven supply chain operating platform covering:

• Inventory Forecasting Demand: Deploy sophisticated predictive models to analyze inventory-specific demand patterns, incorporating seasonality, market trends, and lead-time variability to ensure optimal stock levels and availability.

• Procurement Management: Build automated, intelligent procurement systems that optimize vendor selection, purchase order generation, and cost-efficiency.

• Logistics Operations: Develop optimization engines for route planning, warehouse resource allocation, and real-time distribution efficiency.

• Strategic Leadership: Mentor a team of engineers, championing best practices in code quality, system architecture, and MLOps.

• Workflow Transformation: Utilize Design Thinking to audit, redesign, and automate legacy workflows. Ensure that process re-engineering and AI implementation work in tandem to eliminate bottlenecks.

• Customer & Stakeholder Partnering: Act as a technical authority in client-facing engagements. Translate complex business pain points into scalable technical requirements that deliver immediate business value.

• Cultural Leadership: Embody our corporate culture in all interactions, fostering a collaborative, transparent, and high-performance team environment.

Requirement

• Degree in Computer Science or related discipline, Operations Research, Industrial Engineering or Data Science.

• Experience: At least 5 years of software engineering experience, with 3+ years specifically in AI/ML development. Proven track record of leading technical teams through complex, multi-phase project lifecycles.

• Technical Proficiency: Expert-level Python for both ML modeling and backend systems. Proficient in time-series forecasting (Prophet, LSTMs, Transformers) and constraint-based optimization (OR-Tools, Gurobi). Have sound technical knowledge and skills related to databases/data lakes/data platforms (e.g.Databricks) setup and implementation.

• Domain Expertise: Comprehensive understanding of the integrated supply chain, specifically regarding the nuances of inventory forecasting demand, purchasing, warehousing and last-mile/distribution logistics. Proven record of delivering quantifiable ROI (e.g., reduction in inventory holding costs, improvements in delivery speed, or automated procurement throughput).

• Consulting Mindset: Ability to conduct process discovery, facilitate design-thinking workshops, and articulate technical roadmaps to non-technical executive stakeholders.

• Engineering Rigor: Deep knowledge of cloud infrastructure (AWS/GCP/Azure), containerization, and MLOps pipelines (CI/CD for data and model versions). Direct experience integrating AI solutions with major ERP (SAP, Oracle) or WMS platforms.

• Communication: Exceptional stakeholder management skills; capable of navigating complex conversations with both technical developers and business clients.