Sr. Software Development Engineer, Amazon Pharmacy, Amazon Phamarcy

Join Amazon Pharmacy's Supply Chain Engineering team in Bangalore and build the systems that determine how medications reach patients. You will design and develop ML-driven supply chain technology: demand forecasting models that predict prescription volume, procurement systems that optimize purchasing under expiry and regulatory constraints, placement algorithms that position inventory across fulfillment centers, and planning systems that allocate capacity to meet patient demand. You will work at the intersection of software engineering, operations research, and machine learning.

This is a founding team. You will build new systems from scratch, not maintain legacy code. You will work with large-scale datasets, ML models in production, and distributed systems that must be highly available because medication access depends on them. Pharmacy supply chains are unlike retail: demand is driven by prescriptions (not browsing), products expire, controlled substances require compliance layers, and regulations vary by state. Every system you build operates under these constraints.

We are building an AI-native engineering team. You will use AI-augmented development workflows daily: code generation, automated testing, AI-assisted code review. We expect engineers who learn fast, build smart, and own their systems end-to-end from design through production operations.

Key job responsibilities A. System Design & Development

• Design and build scalable, resilient services for supply chain optimization: forecasting, procurement, placement, or planning
• Develop ML-integrated systems that improve over time: learned demand models, intelligent reorder logic, placement optimization
• Write high-quality, well-tested code and participate actively in code reviews
• Implement operations research techniques in production: optimization solvers, simulation engines, probabilistic demand models, safety stock calculations
• Follow supply chain engineering best practices: backtesting against historical data, offline evaluation before deployment, experiment design for measuring real-world supply chain impact
• Build data pipelines that process large-scale pharmacy supply chain signals: prescription fills, supplier lead times, inventory positions, drug expiry dates

B. Operational Ownership

• Own the systems you build end-to-end: design, development, testing, deployment, monitoring, and oncall