AI Engineer

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

• Design and build multi-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A
• Lead end-to-end delivery of agentic underwriting and claims automations, from prototype through production hardening
• Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable
• Lead offshore engineers and team members on agentic patterns, prompt engineering, and reliability practices
• Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence
• Translate business intent into working agentic systems — not just systems that compile, but systems that deliver measurable business outcomes.

AVP, AI Engineering

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Own and drive the AI Platform roadmap and delivery
• Drive the development of core platform capabilities
• Build a modular, interoperable platform architecture
• Enable downstream product teams to build high-quality AI-driven experiences
• Establish a rigorous evaluation framework for platform performance
• Operate across multiple workstreams simultaneously
• Bring a strong point of view on agentic systems and enterprise AI patterns

Senior Platform Product Manager, AI Platform

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Lead the strategy, architecture, and enablement of the enterprise cloud and AI platform ecosystem
• Partner across product, security, data, and application teams to deliver secure, reliable, scalable cloud capabilities
• Set and communicate the vision, operating model, and multi-year roadmap for Multi cloud & AI Platforms
• Own the developer enablement strategy, including CI/CD patterns and self-service automation
• Define and evolve the enterprise AI platform architecture
• Partner with Security, Risk, and Compliance to embed security-by-design and data protection
• Drive cloud financial management (FinOps)
• Define platform standards and reference architectures and influence AI adoption
• Measure and improve platform outcomes using KPIs
• Represent the platform organization in executive forums

Senior Director, Cloud and AI Platforms

• Own strategy and evolution of platform capabilities that enable AI assistants, workflows, and integrations.
• Define how internal AI systems and external partners connect to FUB data through well-designed tools and integration patterns.
• Shape intelligence capabilities that help AI better understand agent and lead activity.
• Lead product direction for memory capabilities that make AI experiences more personalized over time.
• Partner closely with engineering, AI/ML, design, and product peers to assess tradeoffs and define solutions.
• Help identify and prioritize highest-value platform opportunities based on product needs and long-term strategy.
• Drive alignment across teams by communicating roadmap priorities and progress clearly to stakeholders and leadership.
• Contribute to long-term vision for how AI platform capabilities show up in product experiences across web, mobile, automation, and assistant surfaces.

Senior Product Manager, AI Platform – Ecosystem

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

Senior Software Engineer II, ML/AI Platform

• Own features from concept through production release, writing and shipping high-quality code daily
• Build, test, deploy, and iterate rapidly across frontend, backend, APIs, and data layers
• Troubleshoot and resolve production issues, performance bottlenecks, and complex bugs
• Implement and maintain CI/CD pipelines, automated testing, and deployment automation
• Contribute to architectural decisions and design discussions as a practitioner, ensuring designs translate into solid implementations
• Build and ship modern frontend applications using frameworks such as React, Angular, or Vue.js with TypeScript
• Develop, deploy, and operate robust backend services using Java (Spring Boot), Node.js, Python, or .NET
• Implement and optimize RESTful APIs, GraphQL endpoints, and event-driven integrations
• Configure and manage cloud-native infrastructure (AWS or GCP)
• Build and tune data layers across relational and NoSQL databases, caching, and message queues
• Write clean, well-tested, production-ready code with a focus on maintainability and operational excellence
• Leverage AI coding assistants to dramatically accelerate development workflows
• Use AI tools for code generation, refactoring, test writing, documentation, and code review
• Design and implement RAG pipelines, prompt engineering strategies, and AI-powered features
• Influence engineering culture by evangelizing AI-augmented development practices
• Train and upskill team members on effective use of AI tools in their daily workflows
• Partner with product, design, and data science teams to identify AI-driven opportunities

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