Sr. Software Development Engineer, Amazon Pharmacy, Amazon Phamarcy

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Software Development

Senior Software Development Engineer for Amazon Pharmacy's Supply Chain Engineering team in Bangalore. The role involves designing and developing ML-driven supply chain technology, including demand forecasting, procurement, placement, and planning systems. It operates at the intersection of software engineering, operations research, and machine learning, building new systems from scratch in a regulated pharmacy supply chain environment. The team is AI-native and uses AI-augmented development workflows.

What you'd actually do

  1. Design and build scalable, resilient services for supply chain optimization: forecasting, procurement, placement, or planning
  2. Develop ML-integrated systems that improve over time: learned demand models, intelligent reorder logic, placement optimization
  3. Own the systems you build end-to-end: design, development, testing, deployment, monitoring, and oncall
  4. Partner with Applied Scientists to productionize ML models and experimentation frameworks
  5. Leverage AI tools to accelerate development velocity and improve code quality

Skills

Required

  • 5+ years of non-internship professional software development experience
  • 5+ years of programming with at least one software programming language experience
  • 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience as a mentor, tech lead or leading an engineering team
  • Bachelor's degree in computer science or equivalent

Nice to have

  • 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • 5+ years working in a Supply Chain or related Operations domain with a focus on ML based applications.

What the JD emphasized

  • build new systems from scratch
  • ML models in production
  • highly available
  • expiry
  • regulatory constraints
  • compliance layers
  • regulations vary by state
  • AI-native engineering team
  • AI-augmented development workflows
  • own their systems end-to-end

Other signals

  • ML models in production
  • build new systems from scratch
  • founding team
  • AI-native engineering team