Automating Trust at Scale: How Reena Chandra Is Setting the Standard for Responsible AI Delivery

-Molly Peck

AI is rewriting the rules of digital transformation, but with its scale comes risk. As companies deploy large language models (LLMs) across products and platforms, one silent challenge becomes clear: how do you trust something that keeps learning and changing?

Reena Chandra has built her career answering that question.

With over a decade of experience in quality engineering, automation, and AI validation, Reena has played a crucial role in transforming how AI models are tested, monitored, and trusted, especially when those models are headed to millions of users via tech giants like Apple, Atlassian, and AWS. Her work ensures these models not only ship faster, but ship safer.

From Embedded Testing to LLM Validation

Reena’s journey into AI infrastructure wasn’t born in an ML lab. It began in the trenches of software testing, automating firmware validation for consumer devices, running regression tests for financial systems, and refining performance pipelines for major operating systems. She worked across domains at Apple, eBay, and Broadcom, often at the intersection of product release, infrastructure readiness, and QA under high stakes.

By the time she joined Amazon, her toolkit was broad: mobile testing, hardware validation, REST API automation, Jenkins CI pipelines, Python scripting, and regression frameworks. But her shift to AI testing came with new demands: now she wasn’t just validating buttons and services, she was validating intelligence.

When Model Quality Becomes Business-Critical

Today, Reena works at Amazon as a Senior Software Engineer, where she leads efforts to automate testing and deployment of LLMs and AI models used across internal teams and external clients. These models power summarization, classification, customer insights, recommendation engines and more, feeding directly into AWS services and high-visibility product launches.

One of her most impactful contributions is an end-to-end automated testing framework built specifically for LLM validation. Her system doesn't just run basic pass/fail tests. It evaluates hallucinations, bias, consistency, and performance degradation, tracking how a model's answers evolve across updates.

Even minor changes in a large model can lead to unpredictable regressions. Without deep testing, those changes can slip through to customers, resulting in poor user experiences or broken trust. Reena’s frameworks continuously compare model behavior across multiple builds, flagging discrepancies before they go live. This approach helps teams catch flaws early, avoiding post-release firefighting.

The impact is significant: her work has cut release validation time dramatically and improved model stability across multiple cycles.

Serving Major Clients with Invisible Precision

While AI labs build the intelligence, it’s Reena’s infrastructure that ensures it behaves reliably at scale. Her tools have been used in releases deployed to companies like Apple, Bedrock, and Atlassian, organizations with zero tolerance for broken features or fuzzy models.

To support this, she integrated her validation suite directly into CI/CD pipelines, enabling automated nightly testing with traceable error logs. Her system identifies exactly where and why a model failed a test, empowering machine learning engineers to iterate faster and more confidently.

But this isn’t limited to software. Reena’s expertise in hardware testing, especially with firmware for medical and customer-facing devices, adds a layer of precision to everything she touches. Her work bridges embedded systems and AI infrastructure, giving her a rare vantage point across the full technology stack.

Making Performance Measurable and Accountable

Reena also tackled another persistent challenge in AI delivery: performance benchmarking. Scaling models is expensive. A few milliseconds of extra inference time on a million queries can mean millions of dollars. Reena built a benchmarking system that tracks training and inference performance across environments, GPUs, Trainium chips, and cloud clusters.

Her tools measure throughput, cost, and latency. More importantly, they bring these metrics into early-stage development, giving engineering teams the data they need to tune architectures before they become too costly to change. Her performance dashboards now serve as a single source of truth for multiple project teams.

These dashboards have also proven critical for adoption metrics. Reena designed them to track how customers interact with model-powered features, data that helps prioritize improvements, streamline product fit, and increase overall engagement. The result: smarter iteration, tighter feedback loops, and real-world usage data driving engineering priorities.

Leading with Clarity in Complex Systems

At the core of Reena’s work is a principle that resonates across every role she’s held: build for clarity.

In AI systems, where behavior can be opaque and decisions are often probabilistic, clarity is both a technical and ethical requirement. Reena’s validation platforms don’t just flag issues, they explain them. Her tests provide interpretable output that engineers, product managers, and leadership can use to assess whether a model is improving, regressing, or behaving differently for a reason.

This transparency is essential, particularly when models are evaluated for fairness, safety, or compliance. Reena’s tools support demographic stability testing and edge case detection, ensuring that behavior stays consistent and equitable across user groups. These aren’t theoretical validations; they’re part of production workflows and responsible AI initiatives.

Scaling AI Without Scaling Risk

Perhaps Reena’s most understated accomplishment is the reliability she’s baked into model release processes. While AI often evokes images of cutting-edge breakthroughs, real-world deployment depends on stable, repeatable systems. Reena has delivered exactly that: automated pipelines, regression suites, benchmarking tools, and usability testing all designed to scale safely as models get bigger and demands grow sharper.

Her work has enabled teams to release models faster while reducing reliance on manual review and reactive fixes. Engineers now work with clean dashboards, predictable tools, and validation results they can trust. This level of operational discipline is what allows AI to become a core business function, not just a research asset.

A Career That Mirrors the Evolution of Tech

Reena’s career arc tells a bigger story about how software engineering has evolved. Starting in embedded QA, she moved through enterprise systems, mobile platforms, and web infrastructure, always focusing on validation and performance. But instead of siloing her skills, she applied them laterally, testing device firmware with the same rigor she now brings to machine learning models.

That breadth is what sets her apart. It’s not just that she knows the tools. It’s that she knows how to connect them, build with them, and scale with them without compromising reliability.

Her contributions also reflect the rising need for engineers who understand both systems and strategy. Reena doesn’t just write automation scripts; she enables responsible AI at scale, working across research, engineering, and deployment with a shared sense of accountability.

Building the Infrastructure for Trust

As AI systems become more complex, the margin for error narrows. Reena Chandra’s work is a quiet but critical foundation in this new landscape. Her frameworks validate not just what a model does, but whether it’s doing it responsibly, fairly, and efficiently. Her performance tools ensure speed and cost aren’t afterthoughts. Her dashboards keep teams aligned and decision-makers informed.

In a world that’s increasingly defined by algorithmic decision-making, Reena is building the infrastructure that lets organizations move fast without breaking trust. She’s helping ensure that scaling AI doesn’t mean scaling risk. And in doing so, she’s proving that reliability isn’t the opposite of innovation; it’s what makes it possible.