Ron Jailall

Technical leadership for diverse clients, architecting Agentic AI systems and Hybrid Infrastructure.

• High-Speed AI Agent Framework (Cerebras Project): Developing a novel AI Agent architecture leveraging ultra-fast inference (>1000 tokens/s) to enable real-time, multi-step task decomposition and self-correction. Designed "interruptible" agent workflows that handle ambiguity and user correction in real-time, mimicking human-like latency in complex reasoning tasks.
• On-Device Vision Agent & Hybrid Deployment (Matte Model Project): Architected a CPU-efficient, on-device portrait matting agent (MobileNetV2 backbone) for resource-constrained environments (Electron/Consumer Hardware). Replaced cloud-dependent SDKs with optimized ONNX Runtime inference, reducing latency and enabling offline functionality. Built end-to-end evaluation pipelines on GCP Vertex AI to benchmark model performance, robustness, and accuracy against industry datasets (P3M-10k).
• Scale & Infrastructure: Designed RAG-based agent memory systems using vector embeddings to support personalized, context-aware product recommendations. Migrated Nvidia Riva/Triton microservices to AWS, optimizing for hybrid cloud deployment and ensuring high availability for voice-enabled agent interactions.
• Edge AI Optimization: Led optimization efforts for Nvidia Jetson platforms, utilizing TensorRT to deploy complex computer vision models in strictly resource-limited embedded environments.

Led the R&D team in developing and evaluating GenAI capabilities for production workflows.

• Agent Workflow Evaluation: Established continuous evaluation pipelines to assess prompt robustness, model drift, and agent reliability across multiple LLM backends (OpenAI, Anthropic, Open Source).
• Tooling & Automation: Built internal tools to automate the testing of prompt engineering strategies, significantly speeding up the time-to-adoption for new GenAI features.
• Infrastructure: Architected Terraform/GitLab CI/CD pipelines to deploy inference microservices, supporting both cloud-based and local execution models.
• Strategy: Collaborated with Product Managers and PhD researchers to define the technical roadmap, translating "latest advancements" in diffusion and LLMs into tangible product features.

Engineering leadership focused on large-scale system design and cloud transformation.

• Team Leadership: Mentored and led a distributed engineering team, fostering collaboration to deliver critical updates for a platform serving 100k+ users.
• System Architecture: Designed and deployed a cloud-native archiving utility (AWS Lambda/Batch) that scaled dynamically to handle massive data loads, directly driving millions in revenue.
• Innovation: Founded the company's internal AI reading group and led hackathons to explore early agentic workflows and automated content generation.
• Early LLM Adoption: Prototyped agent-based search tools and successfully deployed GGML Llama models to serverless architectures for cost-effective inference.

• Designed and implemented embedded control systems for 200+ learning spaces, focusing on accessibility and user-centric automation.