Manager, AI & Data Architect – AI Engineer

Role Overview

• Lead data modeling and engineering across advanced data platforms to achieve digital outcomes, including solution designs for Cloud Data Lake, Data Warehouse, Data Marts, and Data APIs, with ownership of enterprise data quality standards across structured, semi‑structured, and unstructured data domains.
• Architect and deliver scalable cloud data solutions leveraging platforms such as Snowflake and associated orchestration/automation patterns; ensure performance, maintainability, reuse across the enterprise, and support for I‑ready workloads including feature and embedding storage.
• Lead the design and implementation of cloud‑native architectures, including microservices‑based solutions, containerized workloads (e.g., Kubernetes), and DevOps practices.
• Ensure data and AI platforms are deployable, scalable, and operable using modern CI/CD pipelines, infrastructure‑as‑code, and automated environment management
• Lead design and implementation of AI models and algorithms (GenAI/LLM-enabled and traditional ML patterns), including model selection/orchestration, agents, RAG-style patterns, and evaluation approaches as appropriate for regulated environments.
• Oversee development and execution of test plans/scripts and thorough data validation; implement automated build/test/deploy/monitoring for ETL pipelines and AI components in a CI/CD environment, including automated data quality checks and observability controls.
• Drive root cause analysis for production data/AI issues; implement observability, monitoring, and preventative controls to improve quality, reliability, and consistency of data products, pipelines, and vector‑based AI systems.
• Collaborate with backend engineering and other technical teams across Digital to deliver end-to-end implementations; document and present methodologies, findings, and outcomes to stakeholders, including data quality metrics and AI solution performance.
• Collaborate effectively with contractors and partners to deliver technical enhancements while maintaining architecture standards, documentation quality, and delivery cadence. Provides guidance and may lead/co-lead moderately complex projects.
• Stay current on AI/ML advancements and apply them to enterprise initiatives; identify reusable components and patterns that accelerate delivery across teams, including unstructured data processing and vector‑based retrieval approaches, that accelerate delivery across teams.

Requirements

• Bachelor’s degree (Master’s preferred) in Computer Science, Data/AI/ML, Engineering, or related field
• 4+ years in data engineering/architecture (or equivalent), including modern data warehousing, modeling, and transformations; 2+ years delivering GenAI & AI/ML solutions in production (preferred)
• Strong Python engineering (production coding, packaging, testing), plus strong SQL
• Snowflake: data modeling, performance patterns, security concepts, and operational usage
• Experience designing and operating cloud‑native platforms , including microservices architectures, containerization (e.g., Kubernetes), and DevOps practices such as CI/CD, automated deployments, and environment management
• Cloud architecture for data platforms (e.g., Azure/AWS), including storage, compute, identity, and networking fundamentals
• Building/operating ETL/ELT pipelines with CI/CD automation, monitoring, and data quality controls
• Demonstrated experience implementing enterprise data quality standards, including validation, observability, monitoring, and lineage across structured, semi‑structured, and unstructured data.
• Experience delivering GenAI & AI/ML solutions end-to-end (feature/embedding generation, model integration, deployment, evaluation)
• Familiarity with LLM concepts and practices (prompting, evaluation, orchestration), and responsible AI considerations for regulated environments
• Hands‑on experience with vector databases and embedding‑based retrieval patterns (e.g., semantic search, RAG, similarity matching)
• Experience working with unstructured and semi‑structured data (documents, text, PDFs, logs, APIs) for analytics and AI use cases
• Proven ability to lead/co-lead moderately complex projects, mentor engineers, and guide contractors to deliver outcomes
• Strong communication skills: can translate technical decisions and tradeoffs to non-technical stakeholders; strong documentation discipline.

Tech Stack

• AWS
• Azure
• Cloud
• ETL
• Kubernetes
• Microservices
• Python
• SQL

Benefits

• 20% travel may be required based on delivery and project priorities
• Work Location Assignment: Hybrid