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 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
Basic Qualifications
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Knowledge of machine learning model architecture and inference
Preferred Qualifications
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
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