Overview In this role you will lead the architectural foundation for MGA's automation and AI-enabled product development. You'll bridge cloud-native engineering with MGA's ERP and data estate, moving prototypes into enterprise-ready production. You'll shape standards and frameworks that support rapid experimentation at scale, while aligning with security and governance. This is a hands-on, leadership-forward opportunity to drive a product-centric, innovative IT culture.
Responsibilities- Define architectural vision, standards, and reference patterns for automation and AI-enabled product development
- Translate technology strategy into actionable frameworks linking rapid experimentation with enterprise readiness
- Design and own the automation sandbox environment with security and compliance for fast iteration
- Manage the prototype-to-production transition pipeline with quality, security, and performance gates
- Own API strategy, microservices, data access frameworks, and cloud infrastructure design
- Architect automation-enabled workflows with solution designers, data engineers, and product teams
- Set standards for model deployment, vector search, data pipelines, and runtime hosting
- Ensure solutions integrate with ERP, CRM, eCommerce, and Data Warehouse
- Bridge modern development practices with the enterprise IT estate and ensure security/compliance
- Mentor full-stack developers, ML engineers, and platform engineers; develop future technical leads
- Publish MGA's Architecture Framework for AI-enabled solution development; build automation sandbox and secure access
- Deliver the transition pipeline and reusable reference architectures for APIs, integrations, and workflows
- Launch multiple AI-enabled solutions across business units; establish architectural guardrails for ERP, data, integration, and cloud alignment
- Support adoption of architectural patterns and automation frameworks across the organization
- Improve developer satisfaction and uplift technical capability across product teams
Key requirements- 7-12 years in software engineering or solution architecture
- Hands-on cloud-native development (AWS, Azure, or GCP), containers, modern app architectures
- Experience deploying LLM-based apps, AI/ML systems, or enterprise automation frameworks
- Strong understanding of ERP, CRM, data platforms, Power Platform, and identity management
- Proficient in DevOps-CI/CD, Infrastructure as Code, observability, secure-by-design
- Experience designing vector search pipelines across multimodal data
- Proficiency with vector databases (Pinecone, Weaviate, pgvector) and evaluating managed vs self-hosted options
- Knowledge of data ingestion, preprocessing, and embedding refresh cycles across heterogeneous sources
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- leadership
- mentorship
- cross-functional collaboration
- AWS, Azure, or GCP cloud platforms
- containers
- LLM-based AI/ML deployments
