This is a brand new role designed to shape the future of our engineering organization. As Senior Staff Developer - AI and Machine Learning, you will serve as the technical anchor for our machine learning and AI efforts, leading the end-to-end architecture of our GenAI strategy. You will bridge the gap between theoretical research and production-grade engineering-designing the scalable systems that power our model training, deployment, and monitoring. We are looking for a rare combination of deep technical mastery and strategic leadership to ensure our AI initiatives deliver high-impact, measurable business value. What you'll do: • Design and oversee the development of robust end-to-end ML architecture, from data ingestion and feature stores to model serving and monitoring. • Define the long-term roadmap strategy for our AI infrastructure and orchestration framework. • Oversee the design, implementation, and maintenance of our AI/ML ecosystem. • Design AI capabilities as extensible, self-service primitives (APIs, SDKs, standardized patterns) that let other domain teams build their own AI-powered features independently. • Set the standard for MLOps - you will ensure that our ML ecosystem is as testable, maintainable, and scalable as our core application code. • Cross-functional leadership - by working closely with Product Managers, Data Scientists and ML Engineers to translate business problems into concrete technical requirements. • Act as a force multiplier for the team by conducting high-level design reviews and mentoring engineers on system design and performance optimization. • Architect and evolve LLM-powered applications (e.g., copilots, search, assistants, agents), including RAG pipelines, tool integrations, and multi-step reasoning workflows. • Design and implement robust evaluation frameworks for GenAI systems, incorporating offline benchmarks, online metrics, and human-in-the-loop feedback. • Drive best practices for prompt engineering, agent design, and orchestration frameworks, ensuring maintainability and performance at scale. • Establish guardrails and safety mechanisms for GenAI applications, including prompt injection defenses, hallucination mitigation, and responsible AI practices. • Establish the golden path for model versioning, A/B testing, and automated rollbacks for identifying and mitigating drifts. • Ensure AI architectural strategy aligns with industry best practices and standards, complies with security policies and industry regulations. • Identify opportunities for process improvements and implement solutions to enhance platform performance and efficiency. What you'll bring: • Bachelor's or Master's degree in Computer Science, Mathematics, or a related field, or equivalent deep professional experience. • 8+ years of software engineering experience, with at least 4+ years architecting and deploying ML models in production at scale. • 2+ years in a technical leadership role on building scalable platforms, technology transformation and modernization initiatives. • Proven experience operating at a Staff or Senior level, including technical leadership, architecture ownership, and mentoring engineers. • Experience building internal platforms/self-service infrastructure for other engineering teams to build on, not just shipping product features directly. • Deep expertise in MLOps and production ML systems, including model training, evaluation, deployment, monitoring, and lifecycle management. • Strong experience with cloud platforms (AWS or Google Cloud), including designing and operating scalable, distributed AI/ML workloads. • Solid understanding of data architecture and data engineering, including data pipelines, feature engineering, data modeling, and large-scale data processing. • Experience with ML infrastructure and tooling, such as feature stores, experiment tracking, model registries, and orchestration frameworks. • Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), with strong software engineering fundamentals. • Experience with CI/CD and ML deployment pipelines, including automated testing, validation, and rollback strategies for ML systems. • Familiarity with LLM-based systems and GenAI applications, including systems design, evaluation strategies, and observability for non-deterministic systems. • Strong understanding of LLM architectures and trade-offs, including model selection, latency, cost, and quality optimization. • Experience with prompt engineering and prompt orchestration, including techniques like few-shot learning, chain-of-thought, and tool/function calling. • Experience designing and implementing RAG (Retrieval-Augmented Generation) systems, including embedding strategies, vector databases, and retrieval optimization. • Experience building agentic workflows, including multi-step reasoning, tool use, and orchestration frameworks (e.g., LangChain, LlamaIndex, ADK, or custom frameworks). • Strong understanding of system design for reliability and scalability, including distributed systems, APIs, and microservices architecture. • Knowledge of data governance, model governance, and responsible AI practices (security, privacy, bias, explainability). • Demonstrated ability to translate ambiguous business problems into scalable AI/ML solutions. • Excellent communication and collaboration skills, with the ability to influence stakeholders and drive cross-functional alignment. Great-to-haves: • Certification in relevant cloud platforms or technologies.