DSS 🦇 ATX | GENAI & INTELLIGENT AGENTS IN THE ENTERPRISE
Join us at Data Science Salon Austin for an exclusive experience focused on GENAI & INTELLIGENT AGENTS IN THE ENTERPRISE
Data Science Salon is a flagship Austin, TX event. The intimate one-day 200 person conference curates data science sessions to bring industry leaders and specialists face-to-face to educate each other on innovative solutions in generative AI, machine learning, predictive analytics and acceptance around best practices. You will see a mix of use-cases, technical talks and workshops, and you will walk away with actionable insights from those working on the frontlines of machine learning in the enterprise. All sessions are recorded and will be available on demand after the event.
Schedule:
🔸 8:30 AM – 10:00 AM Registration Opens + Light Breakfast
🔸 9:55 AM – 10:00 AM Welcome & Opening Remarks
🔸 10:00 AM – 10:30 AM
Keynote: Dushyanth Sekhar, Head of AI & Data Platforms - Enterprise Data Organization at S&P Global
Talk: Multi Model Approach to Large Scale Information Retrieval
As AI adoption grows, relying on a single LLM for data extraction can lead to inconsistency and bias. This session explores how using multiple LLMs in parallel—combined with an automated LLM-as-a-Judge framework—can boost accuracy, reduce bias, and improve trust in extraction pipelines. Learn how to build scalable, resilient systems for unstructured data extraction without heavy human oversight.
🔸 10:30 AM – 10:50 AM | Shamindra Peiris, Senior AI Product Manager at Visa, Inc
Talk: AI Based Product Management
🔸 10:50 AM – 11:20 AM | Devdas Gupta, Senior Manager Software Development and Engineering Lead at Charles Schwab
Talk: Why Agentic AI Works in Demos and Breaks in Enterprise Production
Agentic AI systems look impressive in demos. Agents reason, plan, collaborate, and act with apparent autonomy. Yet when these systems enter enterprise production, most fail under real world constraints.
This session is intended for technology leaders, engineers, and architects involved in designing or delivering Agentic AI systems at enterprise scale. It examines why Agentic AI breaks in production, focusing on architectural boundaries, governance, controlled tool execution, failure isolation, and operational ownership.
Attendees will gain practical insight and proven design patterns to move Agentic AI systems from experimental demos to reliable, secure, and scalable enterprise platforms.
☕️ 11:20 AM – 11:40 AM Coffee Break
🔸 11:40 AM – 12:20 PM | Samaresh Kumar Singh, Principal Engineer at HP Inc.
Talk: Practical Insights on Building Production Edge AI Systems
As AI scales, cloud-only inference often falls short on latency, privacy, and availability. This session presents a production-ready architecture for running AI at the edge—where data is generated—while using the cloud for training and governance. We’ll explore a three-tier cloud–edge–device stack, orchestration patterns (centralized, federated, agent-based), and low-latency techniques like quantization, early-exit inference, and pipeline parallelism. Attendees will walk away with actionable strategies to deploy fast, resilient AI across industrial, healthcare, and smart city applications.
🔸 12:10 PM – 12:40 PM | Hamed Alikhani, Senior AI Engineer & Data Scientist at McGraw Hill
Talk: From Prompts to Production: Building Reliable AI Agents for the Enterprise
Agentic AI is no longer just a proof of concept. This session dives into how enterprises can design and deploy intelligent agents that are reliable, scalable, and safe in real-world environments.
Drawing from real production rollouts, we’ll unpack key architecture patterns—from task decomposition and tool orchestration to memory and routing logic. You’ll learn how to move past brittle prompt chains, implement robust validation layers, and design agents that deliver measurable outcomes while aligning with enterprise governance and cost constraints.
Perfect for technical leaders and practitioners ready to operationalize AI agents at scale.
🔸 12:40 PM – 1:00 PM | Deeksha Mishra, Data Science Manager at Meta
Talk: Coming Soon
🥗 1:00 PM – 2:00 PM Lunch Break
🔸 2:00 PM – 2:45 PM | Panel: From Prototype to Production: Building AI That Actually Works in the Enterprise
Moderated by: Cal Al-Dhubaib, Responsible AI & ML Executive at Further
Christianna Clark, GenAI Engineer at Google
Brent Schneeman, SVP of Artificial Intelligence at The SSI Group
Fatma Tarlaci, Chief AI Officer at Soar.com
Akshay Mittal, Member of Technical Staff at PayPal
🔸 2:45 PM – 3:15 PM | Dippu Kumar SinghLeader Of Emerging Data TechnologiesFujitsu North America Inc.
Talk: Stop Building Generalists: Architecting Task-Specific Agents for Real-World Enterprise ROI
Generic LLMs often fall short in complex enterprise workflows. This session introduces AI Integration—a framework for replacing one-size-fits-all bots with task-specific agents that plug directly into high-value business processes like audits, supply chain ops, and SaaS platforms.
We’ll explore how to use Prompt Chaining, Semantic Routing, and Evaluator-Optimizer loops to design agents that are accurate, composable, and embedded into real systems. You’ll leave with a clear taxonomy of agent types and a practical roadmap for architecting AI systems that drive measurable ROI.
🔸 3:15 PM – 3:35 PM | Reema Gill, Data/AI Governance Specialist at Wealthsimple Technologies
Talk: Scaling Responsibly: Building Data & AI Governance Frameworks for Fintech Startups That Grow into Enterprises
Fintech startups move fast—but as data access, AI adoption, and regulatory exposure grow, governance can’t wait. This session explores how emerging fintechs can implement right-sized AI and data governance frameworks that evolve with scale—without slowing innovation.
Drawing from experience across banks and high-growth startups, we’ll discuss how to operationalize global standards (EU AI Act, NIST AI RMF, OSFI E-23) to build trust with regulators, investors, and customers. The goal: make governance a catalyst, not a constraint.
🔸 3:35 PM – 4:05 PM | Pavan Kumar Mantha, AVP, Principal Data Engineer Lead at Synchrony
Talk: Optimizing Customer Engagement Timing: From Data Pipelines to Best Time to Call Models
Best Time to Call (BTTC) isn’t just a predictive model—it’s an applied AI system that blends behavioral data, ML, and operational constraints to optimize customer contact timing at scale. This session reframes BTTC as a decision intelligence challenge, sharing how financial institutions build end-to-end systems that go beyond traditional model metrics to drive real-world impact. Attendees will gain practical insights on feature engineering, balancing precision with reach, incorporating business KPIs into evaluation, and managing model drift, feedback loops, and fairness in production.
☕️ 4:05 PM – 4:25 PM Coffee Break
🔸 4:25 PM – 4:55 PM | Hari Kishan, Director of Cloud Engineering at Manulife John Hancock Retirement
Talk: Inside Manulife’s Agentic Architecture: Real-Time Voice Automation Powered by RAG and Sentiment AI
While many enterprises talk about conversational AI, few have deployed it at scale—let alone in a highly regulated financial setting. At Manulife, that’s exactly what we’ve done.
This session dives into how a legacy Avaya IVR system was reimagined into an agentic, self-optimizing platform powered by Amazon Connect and RAG pipelines. Learn how we built SSML models that adjust tone and phrasing in real time, reduced AHT, and introduced an orchestration layer that updates strategies in minutes—not weeks.
If you’re scaling GenAI, modernizing CX, or looking to deploy agentic architectures in enterprise environments, this is your blueprint.
🔸 4:55 PM – 5:15 PM | Yukti Goyal, Advanced Software Engineer at FM
Talk: AI for Protecting Patient Data (PHI Security)
As healthcare data grows in volume and value, protecting PHI and ePHI has never been more critical—or more complex. In this session, we’ll explore how AI is transforming cybersecurity in healthcare, with advanced monitoring, anomaly detection, and adaptive defenses tailored for compliance-heavy environments like HIPAA.
Learn how techniques like UEBA, unsupervised learning, and AI-driven threat detection are being used to detect insider threats, stop credential misuse, and build resilient, compliant frameworks for modern healthcare systems.
If you’re working at the intersection of healthcare, AI, and data protection—this session is for you.
🔸 5:15 PM – 5:45 PM | Preetham Kaukuntla, Staff Data Scientist at Glassdoor/Indeed
Talk: Using LLM to Improve Email Marketing
Traditional rule-based notification systems fall short at scale. In this session, Preetham Reddy Kaukuntla shares how Glassdoor rebuilt its email and push infrastructure using ML models for both content and timing optimization. By integrating transformer-based subject line generation, LSTM and Prophet models for send-time prediction, and uplift modeling, Glassdoor now delivers 32M+ daily messages with measurable engagement gains. The session covers architecture, feature engineering, and lessons learned on model evaluation and long-term user impact.
🔸 5:45 PM – 5:50 PM Closing Remarks
🥂 5:50 PM – 7:30 PM Networking Reception
Expect to walk away with tactical insights from those leading AI transformation within their organizations.
About Data Science Salon
Data Science Salon is head quartered in Miami and is the leading provider of unique content and a diverse, vendor-neutral community for data scientists, machine learning engineers, and other subject matter experts. Since 2016, we have been driving knowledge sharing, best practices, and innovation in data science, machine learning and AI. With our extensive digital platform of content, webinars, training, and podcast, we cater to the evolving needs of our fast-growing and diverse community. Join us to access valuable resources, engage in insightful discussions, and be part of shaping the future of data science, machine learning and AI.
Use discount DSSATXLUMA (save 20%)
Refund and Ticket Transfer Policy
Refunds are available up to 30 days prior to the start of the event.
Tickets may be transferred to another attendee up to 48 hours before the event. No exceptions.