AI Architect - Enterprise Integrations

IBM; Paramus (NJ); Remote; USD 150,000 - 210,000; Full time

Job summary

IBM Consulting is seeking an Enterprise AI Solution Architect to design end-to-end AI platforms on Azure, integrating models, data platforms, and core systems. You will define scalable architectures, governance, and observability, guiding delivery teams and shaping reusable assets.

The role emphasizes hands-on Azure OpenAI, Azure ML, and IaC for AI workloads, with opportunities to work across large client engagements from the US.

Qualifications

• 8+ years in enterprise architecture spanning AI/ML and cloud integration.
• Hands-on Azure OpenAI and Azure Machine Learning experience.
• Strong IaC and CI/CD design for AI workloads.
• Experience with observability and monitoring of AI/ML in production.
• Ability to lead architecture discussions with clients and teams.

Responsibilities

• Architect end-to-end enterprise AI solutions integrating models, data platforms and systems.
• Define integration patterns for AI services, data pipelines and business systems.
• Translate requirements into scalable AI-powered designs with tradeoffs and risks.
• Lead architecture design reviews and provide guidance to delivery teams.
• Contribute reusable assets and POVs to strengthen AI delivery capability.

Skills

Enterprise architecture

AI/ML integration

Azure cloud

CI/CD for AI

Observability

Tools

Terraform

Bicep

Azure Synapse

MLOps

Job description

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your role and responsibilities

Enterprise AI Solution Architecture

• Architect end-to-end enterprise AI solutions that integrate AI models, data platforms, and enterprise systems into cohesive, production-grade architectures

• Define integration patterns across AI services, APIs, data pipelines, and core business systems to ensure interoperability and extensibility

• Translate complex business requirements into scalable AI-powered solution designs, with clear articulation of tradeoffs, risks, and value drivers

• Lead architecture design reviews and provide hands-on guidance to delivery teams across active engagements

• Contribute to practice development by building reusable assets, frameworks, and point-of-views that strengthen our AI delivery capability

Platform Engineering & Cloud Infrastructure

• Design and implement Azure-based AI platform infrastructure that supports scalable model deployment, data ingestion, and application integration

• Define infrastructure-as-code standards and CI/CD pipeline patterns for AI workloads, enabling repeatable and auditable deployments

• Architect cloud-native environments optimized for AI throughput, cost efficiency, and operational resilience

• Establish platform engineering standards that enable development teams to build, test, and ship AI solutions faster and more reliably

• Evaluate and recommend emerging platform capabilities, cloud services, and tooling that advance our delivery approach

Observability, Governance & Development Lifecycle

• Design observability frameworks for AI systems including model monitoring, prompt evaluation, drift detection, and performance alerting

• Define AI governance standards covering responsible AI principles, audit trails, explainability requirements, and human-in-the-loop controls

• Establish development lifecycle practices for AI — from experimentation and model validation through to production deployment and ongoing operations

• Implement guardrail frameworks and safety controls for agentic and generative AI workloads to ensure appropriate oversight and auditability

• Champion MLOps and AI engineering best practices across the team, ensuring solutions are maintainable, observable, and continuously improvable

Thought Leadership & Practice Growth

• Serve as a senior technical voice both internally and with clients, sharing expertise, identifying emerging patterns, and helping define our AI point of view

• Mentor architects and engineers on platform engineering, governance, and AI architecture best practices

• Engage in pre-sales and solutioning activities, helping shape proposals and articulate our technical differentiation

This role can be performed from anywhere in the US.

Required technical and professional expertise

• 8+ years of experience in enterprise architecture, with demonstrated expertise spanning AI/ML, cloud infrastructure, and systems integration

• Hands-on experience designing enterprise AI solutions on Azure, including Azure OpenAI, Azure Machine Learning, and supporting data services

• Strong grounding in platform engineering, infrastructure-as-code (e.g., Terraform, Bicep), and CI/CD pipeline design for AI workloads

• Experience building observability and monitoring solutions for AI/ML systems in production environments

• Proven ability to define and enforce AI governance, responsible AI, and compliance frameworks at enterprise scale

• Comfortable leading design conversations with both technical teams and client stakeholders

Preferred technical and professional experience

• Familiarity with agentic AI architectures, orchestration frameworks (LangChain, Semantic Kernel, AutoGen), and RAG pipeline design

• Experience with Azure data platform services including Microsoft Fabric, Azure Synapse Analytics, and Azure Data Factory

• Knowledge of MLOps practices including model versioning, A/B testing, canary deployments, and feedback loop design

• Background contributing to a consulting practice or Center of Excellence — building frameworks, POVs, and reusable assets

• Azure certifications such as Azure Solutions Architect Expert, Azure AI Engineer Associate, or DevOps Engineer Expert

IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.