AI Architect

Nexiva•Wilmington, NC

1d•$80•Onsite

About The Position

This role is for an AI/ML Tech Lead / AI Architect responsible for owning the end-to-end architecture for an AI agent, DSL, and SFMC automation ecosystem. The position involves designing agentic AI systems, backend microservices, APIs, and SFMC integrations, defining DSL schemas, and establishing guardrails for AI-generated workflows. The Tech Lead will guide engineering teams, lead POCs, drive best practices, and ensure alignment with architectural vision. Key areas of focus include AI/ML and agentic systems (frameworks, RAG, LLM fine-tuning), cloud-native architectures on AWS, backend microservices, SFMC integrations, and security, governance, and compliance for AI-generated marketing workflows. Collaboration with business, product, CRM, and marketing operations teams is essential.

Requirements

13+ years of software engineering experience with at least 3+ years in a Tech Lead or Architect role.
Strong background in AI/ML systems, including LLMs, Agentic architectures, Prompt engineering, RAG pipelines.
Experience designing complex distributed systems and workflow automation platforms.
Deep understanding of DSL design, interpreters, ASTs, and compiler concepts.
Strong proficiency in Python, TypeScript, or Java.
Experience with cloud native architectures (AWS/Azure/GCP), containers, and microservices.
Proven ability to lead engineering teams, conduct design reviews, and drive technical decisions.
Excellent communication and stakeholder management skills.
Experience building AI driven workflow automation or autonomous agent systems.
Familiarity with AMPscript and SSJS.
Background in marketing automation, CRM systems, or customer lifecycle design.
Experience with security, compliance, and governance for AI systems.
Prior experience in fixed bid or outcome based delivery environments.
Experience with event driven architectures and messaging systems.

Nice To Haves

AWS Bedrock
OpenAI/Anthropic
Agent SDKs
LangGraph
MCP
A2A
LangSmith (arize phoenix)
AWS
K8s
docker
SRE knowledge

Responsibilities

Own the end to end architecture for the AI agent, DSL, and SFMC automation ecosystem.
Design agentic AI systems, backend microservices, APIs, and SFMC integrations (REST/SOAP).
Define DSL schemas (JSON/YAML) for AI generated workflows, ensuring extensibility, safety, and deterministic execution.
Establish guardrails, validation, simulation, and compliance frameworks for AI generated journeys and campaigns.
Create and maintain system blueprints, including data flow diagrams, integration contracts, and deployment architecture.
Act as the hands on technical lead, guiding AI/ML engineers, DSL engineers, backend developers, and SFMC specialists.
Lead POCs, prototypes, and architectural spikes to validate design decisions and technology choices.
Drive coding standards, design patterns, and best practices across engineering teams.
Conduct architectural reviews, code reviews, and design walkthroughs.
Unblock teams, make technical decisions, and ensure alignment with architectural vision.
Partner with AI/ML teams on agent frameworks, RAG pipelines, embeddings, vectorization, LLM fine tuning, evaluation, and safety mechanisms.
Define prompting strategies, context engineering, and model interaction patterns.
Architect cloud native, highly available systems on AWS using IaC (Terraform).
Oversee backend microservices, orchestration layers, and execution pipelines.
Ensure robust integration with SFMC components: Journey Builder, Email Studio, Data Extensions, Personalization logic, REST/SOAP APIs.
Ensure observability, monitoring, logging, and reliability across all services.
Ensure compliance with security, privacy, and governance requirements for AI generated marketing workflows.
Define architectural controls for safe execution, auditability, and data protection.
Lead performance optimization, scalability planning, and risk mitigation.
Work closely with business, product, CRM, and marketing operations teams to translate requirements into scalable technical solutions.
Communicate architectural decisions clearly to both technical and non-technical stakeholders.