• Design and build production-grade generative AI systems - agentic workflows, multi-step RAG pipelines, and LLM-powered applications integrated with enterprise data and services • Define and implement reusable engineering patterns for prompt management, workflow versioning, structured outputs, tool orchestration, and rollback across production AI services • Apply judgment around model selection and routing, token and latency optimization, cost management, and the appropriate boundaries between AI-driven and deterministic application logic • Continuously evaluate emerging AI models, tools, and architectural approaches, incorporating improvements into existing systems incrementally • Integrate AI systems with enterprise data sources, internal APIs, and platforms to enable reliable, production-ready workflows • Own operational outcomes for production AI systems - reliability, latency, throughput, cost efficiency, and scalability targets • Implement and maintain monitoring, observability, tracing, and alerting frameworks to ensure operational visibility and rapid issue resolution • Design and maintain CI/CD pipelines for deployment, versioning, and release management of AI services • Lead production incident response and root cause analysis, driving systemic improvements that reduce recurrence • Build and maintain automated evaluation pipelines for LLM outputs - prompt regression testing, retrieval quality validation, and failure mode tracking • Implement human-in-the-loop controls, content guardrails, schema validation, and structured output enforcement to ensure trusted and auditable AI outputs • Secure AI systems against prompt injection, data leakage, and unauthorized access, aligning with enterprise compliance and security standards • Own the team's GenAI technical direction - defining and enforcing engineering standards, patterns, and best practices across all GenAI workstreams • Make and defend architectural decisions with clarity, providing the technical rationale needed for the Manager and stakeholders to align and move forward confidently • Work closely with the Manager, GenAI Engineering to receive, refine, and execute on scoped GenAI work - contributing technical judgment to prioritization and tradeoff decisions • Provide hands-on code review and technical guidance to engineers contributing to GenAI workstreams, raising overall quality through direct feedback and demonstration • Champion an iterative delivery culture - shipping incrementally, incorporating feedback, and improving continuously in a regular production release cadence

• Lead research and experimentation on new model architectures, training strategies, and evaluation methodologies for LLM/NLU. • Design, develop, fine-tune, and evaluate specialized LLMs for Concierge and Task Agents. • Develop and optimize ML pipelines for training, evaluation, and deployment (AWS SageMaker). • Architect and maintain inference servers, ensuring low latency and high reliability. • Implement and evolve closed-loop self-learning systems for continuous model improvement. • Drive benchmarking, experiment reproducibility, and documentation quality. • Ensure compliance with data privacy standards throughout the ML lifecycle. • Mentor and support the growth of team members; share expertise via tech talks and guides.

Role Purpose Lead the design, development, and delivery of advanced Natural Language Understanding (NLU) solutions leveraging Large Language Models (LLMs) for text and reasoning tasks. Ensure technical excellence, innovation, and alignment with Omilia’s strategic objectives in conversational AI. Accountabilities - Technical Correctness: Own the technical integrity and quality of NLU solutions, ensuring robust, scalable, and maintainable code and models. - System Quality: Oversee architectural decisions, code reviews, and best practices for LLM-based NLU systems. - Delivery Monitoring: Ensure timely and successful delivery of NLU features and improvements, proactively identifying and mitigating risks. - Autonomy: Operate with high independence, making critical technical decisions and setting standards for the team. - Scope & Complexity: Lead initiatives involving complex reasoning, multi-turn dialogue, and advanced text understanding, often spanning multiple projects or products. - Impact: Drive measurable improvements in NLU accuracy, performance, and customer satisfaction. - Influence/Mentorship: Mentor engineers and data scientists, fostering a culture of technical excellence and continuous learning. Key Responsibilities - Design and implement state-of-the-art LLM-based NLU models for text and reasoning tasks. - Lead technical discussions, architecture reviews, and solution design sessions. - Collaborate with cross-functional teams (Product, Data Science, Engineering) to define requirements and deliver solutions. - Conduct code and model reviews, ensuring adherence to best practices and Omilia standards. - Monitor system performance, troubleshoot issues, and drive continuous improvement. - Stay abreast of advancements in LLMs, NLU, and AI, evaluating and integrating relevant technologies. - Provide technical mentorship and guidance to team members.

Role Purpose Lead the design, development, and delivery of advanced Natural Language Understanding (NLU) solutions leveraging Large Language Models (LLMs) for text and reasoning tasks. Ensure technical excellence, innovation, and alignment with Omilia’s strategic objectives in conversational AI. Accountabilities - Technical Correctness: Own the technical integrity and quality of NLU solutions, ensuring robust, scalable, and maintainable code and models. - System Quality: Oversee architectural decisions, code reviews, and best practices for LLM-based NLU systems. - Delivery Monitoring: Ensure timely and successful delivery of NLU features and improvements, proactively identifying and mitigating risks. - Autonomy: Operate with high independence, making critical technical decisions and setting standards for the team. - Scope & Complexity: Lead initiatives involving complex reasoning, multi-turn dialogue, and advanced text understanding, often spanning multiple projects or products. - Impact: Drive measurable improvements in NLU accuracy, performance, and customer satisfaction. - Influence/Mentorship: Mentor engineers and data scientists, fostering a culture of technical excellence and continuous learning. Key Responsibilities - Design and implement state-of-the-art LLM-based NLU models for text and reasoning tasks. - Lead technical discussions, architecture reviews, and solution design sessions. - Collaborate with cross-functional teams (Product, Data Science, Engineering) to define requirements and deliver solutions. - Conduct code and model reviews, ensuring adherence to best practices and Omilia standards. - Monitor system performance, troubleshoot issues, and drive continuous improvement. - Stay abreast of advancements in LLMs, NLU, and AI, evaluating and integrating relevant technologies. - Provide technical mentorship and guidance to team members.