• Develop and deliver end‑to‑end machine learning solutions, including defining technical requirements, architecting scalable systems, and implementing monitoring, logging, and maintenance workflows. • Collaborate closely with engineers, product managers, clinicians, and cross‑functional partners to build new ML products and enhance existing systems. • Lead the design and implementation of MLOps frameworks, including pipeline development, CI/CD integration, drift detection, retraining workflows, and rollback strategies. • Monitor model performance in production, identify issues, propose remediation steps, and ensure strong test coverage and system reliability. • Utilize contemporary software engineering practices to implement scalable, secure, and maintainable AI/ML systems. • Develop and customize API integrations to enable seamless connectivity between cloud‑based systems and ML services. • Participate in architectural discussions to ensure ML platforms meet compliance, performance, and scalability standards.

• Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases. • Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact. • Prototype machine learning use cases for use in the product, and work with stakeholders to iterate on requirements. • Develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases. • Design and build services, API to enable serving LLM solutions driven data to product use cases.

• Scale Addi’s competitive advantage by building a world-class ML Ops foundation that accelerates the transition from model prototype to production, while ensuring our AI systems from credit scoring to generative agents are resilient, cost-efficient, and seamlessly integrated into our core financial product. • Ensure ML/AI systems can be served reliably in production, maintaining strong operational excellence for availability, latency, and incident response, in partnership with the Data Scientist role for model/agent logic and iteration. • Build and maintain the serving and integration layer for ML/AI solutions (APIs, connectors, asynchronous execution patterns), enabling seamless integration with internal systems and Ops tooling. • Establish clear mechanisms for monitoring and reliability of ML/AI systems in production (dashboards, alerts, core KPIs, regression detection, and data/feature quality checks). • Enable repeatable delivery for ML/AI services through strong engineering practices (CI/CD, testing, release strategies, rollback, and operational runbooks). • Make contributions to our Architecture Decision Records repository by evaluating and proposing platform upgrades for ML/AI systems (e.g., feature serving patterns, workflow orchestration, scalable storage) to improve reliability, scalability, and reuse.

• Build the end-to-end pipeline: recording → transcription → summarization → coaching report • Auto-generate Pre-Diagnosis Reports from survey responses and startup data • Design expert matching algorithms using startup profiles and diagnosis results • Architect and implement Multi-Agent systems • Fine-tune LLMs and train domain-specific models • Design prompts, build evaluation frameworks, manage model versions, and run A/B tests