AI Developer

Chairpersons welcome and opening remarks.

Everyone has an AI proof of concept. Far fewer have AI systems serving millions of requests reliably in production. Engineering leaders discuss what happens after the demo, from LLMOps and infrastructure to observability, developer experience and operational resilience, and the architectural decisions that separate successful AI products from abandoned experiments.

Getting an LLM to answer a question is easy. Building an AI application that remains reliable, observable and maintainable at scale is not. This discussion explores orchestration, evaluation, context engineering, memory and platform design, and why production AI is becoming an engineering discipline in its own right.

Traditional software testing wasn’t designed for probabilistic systems. Learn how engineering teams are building evaluation pipelines, automated benchmarks and observability frameworks that detect regressions before users experience them to ensure quality and business performance.

As foundation models become increasingly capable, competitive advantage is shifting away from model selection and toward how applications manage context. This session explores the engineering patterns behind reliable AI systems, including context windows, retrieval strategies, memory, tool use and prompt orchestration. Learn how engineering teams are designing context pipelines that improve accuracy, reduce hallucinations and create more predictable AI behavior in production.

Inference has rapidly become one of the largest operational costs in AI. This session examines the techniques engineering teams are using to optimize model serving infrastructure, improve GPU utilization, route requests intelligently and balance model quality against latency and cost without compromising user experience.

The Reality Gap: Solutions that look simple on paper or video often fail to work in a real enterprise.

Here are a few of the challenges we came across and how we solved them:

• RAG- Strict Knowledge Boundaries: Restricting AI answers solely to internal company data.
• AI AGENT- Production-Ready Text-to-SQL: Refining natural language queries to work reliably on live, messy databases.
• AI AGENT- Format-Preserving Translation: Delivering high-fidelity translations without losing document layout.

AI agents are moving rapidly from experimentation into enterprise applications, but reliability remains the biggest engineering challenge. Engineering leaders discuss planning, orchestration, tool use, memory and failure recovery to build dependable AI agents.

Successful AI programs depend on more than powerful models – they require internal platforms that enable developers to experiment, deploy and operate AI safely and efficiently. This session explores the principles behind effective AI platform engineering, covering APIs, reusable components, governance, developer experience and self-service infrastructure that accelerate AI delivery across engineering teams.