Design and deploy production-grade ML models, implement a company-wide data strategy, and lead AI architecture efforts to innovate advertising technology in a fast-paced environment.
AI Strategy & Integration: Design, train, and deploy production-grade ML models (using AWS SageMaker, Bedrock, Comprehend) for predictive analytics, anomaly detection, recommendation engines, and automation.
MLOps & Model Serving: Own the entire ML model lifecycle, from training and versioning to secure, low-latency deployment (Model Serving) on AWS.
Data Strategy & Governance: Define and implement a company-wide data strategy, including governance, quality, lineage, and access control.
Partner with the Domain Expert to formalize AI-driven rules within the BRD.
AI Governance: Implement robust monitoring for model drift, bias, and performance.
Explainability (XAI): Develop and expose "explainability" endpoints for critical AI decisions (e.g., campaign rejection) to support governance and the Agentic Economy.
Collaboration: Work with the Platform Back-End Dev to define data pipelines and API contracts for real-time model inference.
Leadership: Proven experience building and leading centralized Data/AI teams, including data engineering, data science, and MLOps functions.
Cloud MLOps: 5+ years of experience deploying ML models into production on AWS (SageMaker, MLOps pipelines).
SaaS AI Deployment: Experience deploying AI-powered features into customer-facing SaaS platforms, from recommender systems to intelligent assistants.
API Serving: Proven expertise in exposing ML inference via scalable, low-latency REST APIs.
Data Pipelines: Strong background designing and managing high-volume data pipelines (e.g., Kinesis, Kafka, Spark) for feature engineering.
Model Governance: Experience implementing model monitoring, drift detection, and XAI frameworks, including establishing an AI governance framework with model inventory, explainability standards, and ethical review processes.
Statistical Modeling: Advanced degree (MS/PhD) in Computer Science, Statistics, or a quantitative field.
Strategy: Demonstrated ability to translate AI capabilities into product strategy, driving alignment with Product, Engineering, and Business teams.
Data Security & Privacy: Deep understanding of data security, PII handling, and privacy-by-design (GDPR, CCPA, SOC2) as applied to ML systems.
Upper-Intermediate level of English or higher
Preferred Qualifications:
AWS Certified Machine Learning Specialty.
NLP & GenAI: Experience with NLP for content analysis (e.g., creative validation) or LLM integration, including LLMOps, GenAI safety, prompt versioning, and evaluation.
Experience with probabilistic programming or causal inference.
Personal profile:
Strong leadership and an ownership mindset
Ability to innovate in mature, large-scale systems
Comfortable working in international, cross-functional teams
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