• 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

• Architect & ship: Design end-to-end Python services (batch + streaming) that ingest model outputs, run constrained optimization, and surface real-time dispatch decisions. • Model & simulate: Build/extend ML models (gradient-boosting, deep learning, OR-Tools) and run time-horizon simulations to quantify cost vs. service-level trade-offs. • Operationalize: Automate training, validation, A/B rollout, and monitoring (SageMaker / Airflow). • Lead & collaborate: Partner with Product, Ops, and Data Engineering; mentor a small squad of ML engineers; present findings to execs. • Continuously improve: Instrument NPS / cost telemetry, identify failure modes, and iterate.