Senior AI Architect

https //alliedsolutions.wd501.myworkdayjobs.com/en-US/Allied_External/job/Senior-AI-Architect_R-010462 For JD, please refer to this link For JD, please refer to this link Title: Senior AI Architect Location: Carmel IN (5 days a week onsite role) (Local candidates preffred) The Senior AI Architect is a responsible for shaping the enterprise-wide AI architecture vision and driving the design of scalable, ethical, and high-impact AI solutions. Successful performance requires strong systems thinking, business-facing communication, solution architecture discipline, and practical hands-on experience with AI/ML, GenAI/LLMs, automation, integration patterns, and modern software engineering Job Duties And Responsibilities Enterprise AI Solution Architecture & Design – 55% Partner with the AI Portfolio & Product Manager, business leaders, operations leaders, product owners, and subject matter experts to understand current-state workflows, pain points, decision points, system constraints, data availability, integration needs, and desired business outcomes.Assess prioritized or emerging AI opportunities for technical feasibility, data readiness, architecture implications, integration complexity, security, governance, operational support, and delivery risk.Translate business context into technical assumptions, solution options, architectural tradeoffs, implementation considerations, and readiness recommendationsShape practical AI-enabled workflow concepts that move work from manual execution, rules-heavy processes, and exception-driven operations toward intelligent systems with human oversight, feedback loops, and continuous improvement. Define enterprise-grade architecture for AI-enabled solutions, including business process fit, data needs, AI/model approach, system interactions, integration patterns, security, human oversight, monitoring, and operational supportCreate solution artifacts such as target-state workflows, context diagrams, data flows, decision flows, integration designs, and architecture decision recordsEvaluate and recommend appropriate AI and automation solution patterns, including traditional machine learning, predictive models, optimization, generative AI, retrieval-augmented generation, agentic workflows, workflow automation, rules-based components, or hybrid approaches based on business need, data readiness, feasibility, risk, scalability, and maintainabilityDesign solutions for scalability, reliability, observability, privacy, compliance, supportability, responsible AI guardrails, and long-term operational ownershipCollaborate with enterprise architecture, data architecture, security, compliance, and governance stakeholders to align AI solutions with enterprise standards and delivery expectations. Hands-On Prototyping, Technical Validation & Delivery Enablement – 30% Build or directly contribute to proofs-of-concept, prototypes, technical spikes, and reference implementations to validate feasibility, test assumptions, compare approaches, and de-risk delivery.Translate architecture decisions into practical implementation guidance, reusable patterns, sample components, and working examples that AI Engineers and delivery partners can build fromEvaluate AI services, frameworks, platforms, orchestration patterns, model evaluation approaches, vector databases, integration approaches, and automation tools where needed to establish practical, reusable solution patternsSupport early implementation, design reviews, code reviews, and complex troubleshooting when ambiguity, integration complexity, model behavior, security, responsible AI requirements, or emerging AI capabilities require senior technical judgment.Mentor engineers, analysts, and business partners through hands-on collaboration, technical coaching, and practical decision support AI Architecture Standards, Reuse & Continuous Improvement – 15% Establish and evolve reusable AI architecture standards, reference architectures, implementation patterns, and design playbooks that improve consistency, reduce one-off experimentation, and accelerate delivery.Define practical architecture guidance for responsible AI, including privacy, transparency, explain-ability, auditability, human oversight, exception handling, and model lifecycle considerationsCreate reusable practices for solution evaluation, monitoring, feedback loops, model performance review, operational support, and continuous improvementAssess emerging AI/ML, GenAI, agentic AI, automation, and cloud capabilities for practical application within Allied’s enterprise architecture and operating modelCapture lessons learned, patterns, anti-patterns, and implementation guidance from delivery work and translate them into reusable standards, architecture reviews, and team enablement materials Qualifications (Education, Experience, Certifications & KSA) Bachelor’s degree in Computer Science, Data Engineering, or a related technical discipline required. Master’s degree preferred.10+ years of software engineering or architecture experience, with at least 5 years in AI/ML architecture and solution leadership.Deep knowledge of AI/ML system design, including data pipelines, model lifePractical experience with LLM deployment, vector databases, RAG architecture, or similar emerging AI capabilities, cycle management, MLOps, and cloud-native deployments.Strong expertise with platforms such as Azure Machine Learning, AWS SageMaker, Google Vertex AI, Databricks, and OpenAI APIs.Demonstrated experience leading cross-functional teams and influencing enterprise-wide architecture decisions.Prior experience contributing to AI governance frameworks or responsible AI initiatives.Familiarity with enterprise security, data privacy laws, and risk management practices related to AI.Enterprise architecture certification (e.g., TOGAF, Zachman) is a plus.Strong organizational skills and attention to detail.Relevant certifications such as AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, or similar credentials are preferred but not required.