AI Architect - Internal Business Applications

Role overview Define and own the reference architecture for AI and intelligent automation powering internal business operations. The role blends deep technical design with hands-on engineering to deliver platform capabilities, secure AI workflows, and enterprise-wide transformation across core functions such as Finance, HR, Operations, and Customer Success.

Responsibilities - Set and evolve architectural vision, principles, standards, and governance for AI-first capabilities including LLM orchestration, agentic workflows, and enterprise integrations. - Develop reference architectures covering AI runtimes, model serving, security controls, identity management, policy enforcement, and secure enterprise data access. - Translate operational requirements from Finance, HR, Operations, Customer Success, and adjacent functions into scalable solution architectures and rollout strategies. - Design compliant intelligent automation and decision-support solutions spanning document processing, data extraction, workflow automation, and business process optimization. - Establish enterprise data integration strategies, governance practices, and data quality standards that prepare ERP, CRM, HCM, financial, and operational platforms for AI workloads. - Build APIs, reusable services, and foundational platform components that accelerate AI adoption. - Advance MLOps and LLMOps capabilities including pipeline standardization, model observability, monitoring, lifecycle management, and production governance.

Requirements - 8+ years in software architecture, platform engineering, AI/ML systems design, data architecture, or closely related disciplines. - 4+ years designing and deploying production-scale AI systems such as large language models, agent orchestration frameworks, and intelligent automation solutions. - Deep expertise in modern AI architectures including LLM serving, Retrieval-Augmented Generation (RAG), agentic orchestration, and AI workflow design. - Hands-on proficiency with Python, SQL, AWS/Azure/GCP, Docker, Kubernetes, API and distributed systems design, MLOps/LLMOps tooling, and modern data platforms like Databricks and Snowflake. - Executive-level communication and stakeholder management skills with the ability to translate complex technical concepts for varied audiences. - Strong grasp of enterprise security, identity and access management, data governance, privacy, and compliance frameworks.

Nice to have - Background in fintech, financial services, or enterprise SaaS environments. - Hands-on experience with LLM fine-tuning, prompt engineering, RAG systems, and vector databases. - Familiarity with enterprise application ecosystems (ERP, CRM, HCM) and integration patterns. - Exposure to event-driven architectures, real-time data processing, or workflow orchestration platforms. - Understanding of AI governance, model monitoring, drift detection, and responsible AI frameworks.