AI Infrastructure Architect

About the job

Responsibilities

• Architect and build custom AI infrastructure and hardware solutions.
• Optimize AI infrastructure/hardware performance, power consumption, cost, and scalability.
• Advise on AI infrastructure technology and vendor evaluation, selection, and full-stack integration.
• Own the complete AI platform architecture spanning classical machine learning, generative AI, LLM applications, RAG, agentic systems, context engineering, model platforms, inference, and enterprise AI integration.
• Connect business goals, industry priorities, and transformation agendas to a coherent technical vision and sequenced AI implementation roadmap.
• Ensure AI architectures address domain realities, regulatory expectations, security constraints, operational processes, and measurable business outcomes.
• Lead and integrate work across multiple domain architects and subject matter specialists.
• Shape enterprise AI strategy and convert business priorities into phased technical roadmaps.
• Lead enterprise AI architecture assessments, target-state definition, gap analysis, platform selection, modernization opportunities, and implementation sequencing.
• Set the enterprise direction for lakehouse-native AI platforms on Databricks, defining governed RAG and multi-agent architectures.
• Establish model serving, evaluation, tracing, and monitoring patterns through MLflow and Databricks workflows.
• Guide architecture decisions for data governance, scalability, and cost control.
• Own the end-to-end technical solution for complex AI platforms.
• Define architectural direction for model- and tool-agnostic multi-agent systems.
• Establish the enterprise context layer architecture spanning knowledge graphs, ontologies, vector search, semantic retrieval, prompt/context assembly, and conversation state management.
• Set security, governance, observability, performance, scalability, and reliability standards across AI solution domains.
• Mandate productized evaluation and observability practices covering accuracy, relevance, groundedness, latency, model quality, cost, safety, reliability, and production support metrics.
• Drive architecture decisions for high-throughput, low-latency inference, model routing, model adaptation/fine-tuning, caching, cost controls, and production-ready deployment platforms.
• Produce and own authoritative architecture artifacts including blueprints, ADRs, sequence diagrams, design specifications, integration patterns, reusable reference architectures, and governance playbooks.

Requirements

• Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
• Minimum 12+ years of overall experience in software engineering, data engineering, AI/ML engineering, cloud architecture, or enterprise technology architecture.
• Minimum 8+ years of experience designing and deploying enterprise-grade advanced AI, data, analytics, or cloud-native solutions using at least one cloud vendor.
• Minimum 2+ years of experience in LLM and generative AI solution architecture, including agentic AI, RAG, prompt engineering, model integration, and evaluation patterns.
• Minimum 2+ years of experience architecting and operationalizing LLM-driven application architecture patterns in production or enterprise-scale environments.
• Minimum 6+ years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering, or large-scale analytical engineering applications.
• Demonstrated experience as a senior architect in industry domains such as financial services, healthcare, manufacturing, retail, telecom, or life sciences.
• Ability to align technology choices to business, risk, and compliance expectations.
• Deep architecture and hands-on engineering experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow tracing/evaluation, Vector Search, Unity Catalog, Delta Lake, Delta Live Tables, Lakehouse Monitoring, Feature Store, Databricks Workflows, Jobs, and Model Training.
• Strong ability to shape enterprise-grade AI architecture covering RAG, embeddings, vector databases, semantic retrieval, context engineering, multi-agent orchestration, tool calling, memory, model routing, and GenAI evaluation.
• Experience defining NFRs and architecture controls for performance, scalability, security, privacy, governance, observability, resiliency, cost optimization, and operational readiness.
• Ability to compare platforms, frameworks, foundation models, and deployment patterns, making evidence-based technology recommendations and defensible architecture trade-offs.
• Experience establishing AI evaluation and observability practices.