AI Architect

Job description

About Position: We are looking for an experienced AI Architect to lead enterprise AI assessment engagements. The role will be responsible for evaluating the organization’s AI readiness, identifying high-value AI use cases, assessing data and technology maturity, and defining a practical roadmap for responsible, scalable, and business-aligned AI adoption. The ideal candidate should combine strong enterprise architecture experience with hands-on knowledge of artificial intelligence, generative AI, machine learning, data platforms, cloud architecture, security, governance, and responsible AI practices. • Role: AI Architect • Location: All Persistent Locations • Experience: 10+yrs • Job Type: Full-Time Employment What You'll Do: • Lead AI discovery and assessment workshops with business, technology, data, security, compliance, and operations stakeholders. • Assess current AI maturity across strategy, use cases, data readiness, technology landscape, governance, skills, operating model, and delivery processes. • Evaluate existing business processes and identify opportunities where AI, machine learning, generative AI, automation, or intelligent agents can create measurable value. • Assess data quality, availability, lineage, security, privacy, integration patterns, and platform readiness required to support AI solutions. • Review current enterprise architecture, application landscape, cloud platforms, APIs, data pipelines, model lifecycle practices, and DevOps or MLOps capabilities. • Define AI solution architecture options, including conceptual target architecture, platform components, integration patterns, deployment approach, and scalability considerations. • Evaluate AI risks related to bias, explainability, privacy, security, reliability, hallucination, model drift, regulatory compliance, and human oversight. • Recommend responsible AI and governance controls across the AI lifecycle, including use-case intake, risk classification, model validation, monitoring, auditability, and accountability. • Prioritize AI use cases using business value, feasibility, data readiness, risk, complexity, and implementation effort as decision criteria. • Create an AI adoption roadmap covering quick wins, foundational capabilities, platform improvements, operating model changes, and phased implementation recommendations. • Prepare executive-ready assessment reports, architecture recommendations, maturity scorecards, business case inputs, and implementation plans. • Collaborate with data scientists, data engineers, cloud architects, security teams, business analysts, product owners, and senior leadership to align AI initiatives with enterprise goals. Expertise You'll Bring: • 10+ years of experience in technology architecture, enterprise architecture, solution architecture, data architecture, or AI/ML solution delivery. • Strong understanding of AI, machine learning, generative AI, natural language processing, computer vision, predictive analytics, intelligent automation, and agentic AI concepts. • Experience assessing enterprise AI maturity, AI readiness, data readiness, business process readiness, and technology platform readiness. • Hands-on experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud, including AI/ML services, data platforms, APIs, identity, security, and integration services. • Knowledge of data platforms, data lakes, lakehouses, warehouses, ETL/ELT pipelines, streaming, metadata management, master data management, and data governance. • Familiarity with MLOps, LLMOps, model lifecycle management, model registry, versioning, monitoring, drift detection, testing, validation, observability, and deployment automation. • Understanding of responsible AI principles including fairness, transparency, privacy, security, reliability, safety, inclusiveness, and accountability. • Ability to evaluate AI vendor platforms, foundation models, open-source models, copilots, AI accelerators, and build-versus-buy options. • Strong consulting, stakeholder management, workshop facilitation, problem-solving, and executive communication skills. • Ability to translate business needs into AI opportunities, target architecture, prioritized roadmap, and implementation recommendations. • Preferred Technical Knowledge • Experience with Microsoft Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, Microsoft Fabric, Databricks, Snowflake, AWS Bedrock, Amazon SageMaker, Google Vertex AI, or similar platforms. • Knowledge of large language model patterns such as retrieval-augmented generation, prompt engineering, fine-tuning, embeddings, vector databases, semantic search, grounding, evaluation, and guardrails. • Experience defining AI reference architectures, reusable patterns, platform blueprints, and implementation standards for enterprise environments. • Understanding of regulatory and governance frameworks such as NIST AI Risk Management Framework, ISO/IEC 42001, GDPR, industry-specific compliance requirements, and enterprise risk management practices. • Experience with security and privacy controls such as role-based access control, data masking, encryption, key management, secure APIs, audit logging, and secure model access. • Current-state AI maturity and readiness assessment. • Business process and use-case opportunity assessment. • Data readiness, platform readiness, security readiness, and governance readiness findings. • Prioritized AI use-case portfolio with value, feasibility, risk, and effort scoring. • Target-state AI architecture and recommended platform capabilities. • Responsible AI, governance, risk, and compliance recommendations. • AI adoption roadmap with quick wins, foundational capabilities, and phased implementation plan. • Executive summary, assessment report, presentation deck, and implementation recommendations. • Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, Information Technology