How Mondelez Uses Amazon Q Developer for 2-3x Engineering Productivity
Mondelez International, one of the world’s largest snack companies with products in 150+ countries, selected AWS as its strategic cloud provider in late 2024 and deployed Amazon Q Developer as its agentic AI coding assistant for global engineering teams. The result: engineers experience 2–3x productivity gains, new environment setup dropped from days to under an hour, and server provisioning shrank from 7–10 days to 20 minutes. New engineers who previously took months to become effective team contributors now reach full productivity in days.
Impact
2–3x
Engineering productivity gain
20 minutes
Server provisioning time
<1 hour
New environment deployment time
Days vs. months
Engineer onboarding time
Up to 60%
Energy reduction
Challenge
Mondelez needed to onboard cloud engineers rapidly at scale and enable its existing engineering team to work with greater speed and agility, but legacy infrastructure, manual provisioning workflows, and the complexity of its global cloud environment made both goals difficult to achieve.
Solution
Mondelez deployed Amazon Q Developer as an agentic AI coding assistant for its entire developer organization, integrating it with a custom MCP for internal infrastructure-as-code repositories and using AWS Graviton and Amazon Aurora to modernize the underlying platform.
Tools & Technologies
Amazon Q DeveloperWhat Leaders Say
“Our engineering team experience 2-3X productivity gains by using Amazon Q Developer. The service helps us quickly go from an idea to a working prototype extremely fast.”
“We’ve gone through a period of rapid growth and onboarding many engineers in a short space of time and getting them up to speed as quickly wouldn’t be possible without an AI assistant like Amazon Q Developer.”
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Full Story
Mondelez International manages an iconic portfolio of snack brands—Oreo, Ritz, Cadbury, Toblerone—sold across more than 150 countries. Behind the consumer-facing brands, the company operates a complex global technology stack that, until recently, was constrained by legacy infrastructure, fragmented security policies, and manual engineering workflows that made provisioning a single server a weeks-long process.
The company’s IT transformation began with migrating over 1,000 servers from on-premises and legacy cloud environments to four AWS Regions, replacing Windows/SQL Server workloads with Amazon Aurora Postgres and leveraging AWS Graviton processors for up to 60% energy reduction. But infrastructure modernization alone wasn’t enough. Mondelez faced a second, harder challenge: recruiting and onboarding cloud engineers fast enough to sustain ambitious growth targets, and giving existing engineers the tools to move with genuine agility.
To address the talent and productivity gap, Mondelez deployed Amazon Q Developer, an agentic AI coding assistant, starting with a small team pilot before expanding it to the entire developer organization. Engineers use the tool across any project in the company’s environment, with security guardrails managed through encryption keys, an internal AI review board, and centralized configuration that disables training on external data. A custom MCP (Model Context Protocol) was built in-house, connecting Amazon Q Developer to internal infrastructure-as-code module repositories so engineers can retrieve exact templates through natural language queries.
The outcomes were transformative. Engineers reporting 2–3x productivity gains cite the assistant’s value not just for code generation, but as an on-demand tutor that explains AWS services, validates code before deployment, and helps navigate the company’s existing codebase. Fully compliant server provisioning, which previously took 7–10 days, now completes in approximately 20 minutes. New team members who previously required months to reach productivity now contribute effectively within days.
Mondelez is now positioned as a benchmark for AI-augmented cloud engineering in the consumer packaged goods sector. The company’s fully automated cloud platform can deploy a complete new application environment—including account creation, networking, and compliant EC2 instances—in under an hour with zero manual intervention. As the company expands its MCP capabilities and integrates observability tools into the AI assistant workflow, the model of AI-native engineering is becoming standard practice rather than innovation.