Senior AI Architect

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 recommendations

* Shape 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 support

* Create solution artifacts such as target-state workflows, context diagrams, data flows, decision flows, integration designs, and architecture decision records

* Evaluate 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 maintainability

* Design solutions for scalability, reliability, observability, privacy, compliance, supportability, responsible AI guardrails, and long-term operational ownership

* Collaborate 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 from

* Evaluate AI services, frameworks, platforms, orchestration patterns, model evaluation approaches, vector databases, integration approaches, and automation tools where needed to establish practical, reusable solution patterns

* Support 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 considerations

* Create reusable practices for solution evaluation, monitoring, feedback loops, model performance review, operational support, and continuous improvement

* Assess emerging AI/ML, GenAI, agentic AI, automation, and cloud capabilities for practical application within Allied's enterprise architecture and operating model

* Capture 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 life Practical experience with LLM deployment, vector…