·Architect, build, and deliver solution architectures for content & digital platforms and the Marketing AI platform — hands-on writing production-grade backend code (Python, Node.js), APIs (REST/GraphQL), data pipelines, and infrastructure-as-code, setting the engineering standard for the team.
·Lead the end-to-end Azure-to-AWS migration of the Marketing AI platform — including service mapping, data migration, workload re-platforming, and cutover — and design the target-state AWS architecture using Bedrock, SageMaker, Lambda, Step Functions, ECS/EKS, API Gateway, DynamoDB, and EventBridge.
·Build and deploy agentic AI applications — autonomous marketing agents for content generation, audience segmentation, campaign orchestration, and real-time personalization — using frameworks such as LangChain, LangGraph, CrewAI, or custom orchestration, with proper governance including human-in-the-loop checkpoints and responsible AI practices.
·Drive the north-star vision of automating end-to-end marketing workflows — from insight generation and content creation through approval, distribution, channel activation, and performance measurement — to increase speed-to-market and enable personalized marketing at scale.
·Own DevOps and cloud infrastructure — design and maintain CI/CD pipelines (GitHub Actions, CodePipeline, Jenkins), manage Infrastructure-as-Code (Terraform, CDK/CloudFormation), container orchestration (ECS/EKS/Kubernetes), serverless deployments, environment strategy (dev/staging/prod), and enforce best practices for automated testing, blue/green deployments, monitoring, logging, and incident response.
·Ensure platform security, compliance, and cost optimization — hands-on with IAM, secrets management, VPC/network security, encryption, and data privacy controls; monitor resource utilization, implement auto-scaling, right-size compute, and establish cost governance.
·Lead full-stack and backend engineering — design and build microservices, event-driven architectures, database solutions (PostgreSQL/RDS, DynamoDB/DocumentDB), data pipelines (AWS Glue, PySpark, Airflow), and AI/ML pipelines (SageMaker, Bedrock); build frontend components (React.js, TypeScript) as needed.
·Implement robust integration patterns connecting the Marketing AI platform and content/digital platforms with the broader ecosystem (Veeva, AEM, DAM, CRM, CDP, analytics tools) via APIs, event streams, and middleware such as MuleSoft or webMethods.
·Lead PoCs and pilots—from ideation to working prototype—for agentic marketing use cases (e.g., AI-driven personalization, automated MLR pre-checks, intelligent content repurposing), and scale successful pilots to production.
·Lead by example — actively contributing code, conducting architecture reviews, pair-programming, and providing hands-on technical mentorship to elevate team engineering quality and maintain alignment with architectural standards and best practices.
·Manage technical roadmaps, architecture documentation, and CLAD processes — ensure phased delivery with minimal business disruption, clear rollback strategies, and measurable success criteria; drive continuous improvement for productivity, cost savings, and engineering velocity.