Key Responsibilities

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.

Professional Background and Experience

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 87, 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, andprivacy-by-design (GDPR, CCPA, SOC2) as applied to ML systems.

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.