Posted 28 May, 2026
Software Development Engineer, Amazon Pharmacy, Amazon Phamarcy
Amazon
IN, KA, Bengaluru
Full Time
Reference: 71_654249_257b50bd-0e42-47f1-9ce5-ed09d71b3f8d
oin 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
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
Build robust observability: metrics, alarms, dashboards that surface supply chain health in real time
Participate in oncall rotations and drive root-cause analysis for production issues
Design for failure: implement graceful degradation, circuit breakers, and fallback strategies for mission-critical services
C. Collaboration & Growth
Partner with Applied Scientists to productionize ML models and experimentation frameworks
Work with product managers to translate business problems into technical designs
Collaborate across time zones with US-based teams on priorities, design reviews, and operational handoffs
Contribute to a learning culture: share knowledge, mentor peers, and drive engineering best practices
D. Innovation
Leverage AI tools to accelerate development velocity and improve code quality
Identify opportunities for automation and ML within your domain
Propose and execute on technical improvements that reduce operational toil or improve system performance
Stay current with advances in supply chain ML, optimization, and distributed systems
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
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
Build robust observability: metrics, alarms, dashboards that surface supply chain health in real time
Participate in oncall rotations and drive root-cause analysis for production issues
Design for failure: implement graceful degradation, circuit breakers, and fallback strategies for mission-critical services
C. Collaboration & Growth
Partner with Applied Scientists to productionize ML models and experimentation frameworks
Work with product managers to translate business problems into technical designs
Collaborate across time zones with US-based teams on priorities, design reviews, and operational handoffs
Contribute to a learning culture: share knowledge, mentor peers, and drive engineering best practices
D. Innovation
Leverage AI tools to accelerate development velocity and improve code quality
Identify opportunities for automation and ML within your domain
Propose and execute on technical improvements that reduce operational toil or improve system performance
Stay current with advances in supply chain ML, optimization, and distributed systems
