Key Responsibilities

Lead the architecture, design, and technical roadmap for enterprise AI and AI-native solutions.
Translate business and functional requirements into scalable AI solution architectures across data, model, application, and integration layers.
Evaluate, select, and integrate the latest AI models and LLMs, including cloud and third-party AI services.
Define reusable reference architectures, design patterns, and standards for AI solution delivery.
Collaborate with data engineers, MLOps engineers, application developers, and product teams to deliver production-grade AI solutions.
Define and enforce non-functional requirements, including performance, security, reliability, scalability, and observability.
Conduct technical reviews, proof of concepts (PoCs), and feasibility assessments for emerging AI use cases.
Provide architectural leadership, mentoring, and technical guidance while driving innovation and continuous improvement.

Required Qualifications

Strong experience designing and delivering enterprise-grade AI solutions.
Proven expertise in end-to-end AI solution architecture and reference architecture design.
Hands-on experience with machine learning, deep learning, and LLM-based solutions.
Deep understanding of the latest AI models, their capabilities, limitations, and enterprise use cases.
Experience with cloud AI/ML platforms such as Azure AI, AWS AI/ML, Google Cloud AI, or equivalent.
Strong understanding of data architecture, including data pipelines, feature stores, model deployment, monitoring, and MLOps.
Experience with application integration using APIs, microservices, and event-driven architectures.
Ability to create architecture documentation such as HLDs, LLDs, sequence diagrams, and data flow diagrams.
Excellent communication, stakeholder management, and leadership skills.

Experience with AI governance, responsible AI, model risk management, explainability, security, and privacy.
Familiarity with vector databases, semantic search, Retrieval-Augmented Generation (RAG), and knowledge graph solutions.
Experience with MLOps tools and CI/CD pipelines for AI and ML applications.
Knowledge of cloud-native architectures using Docker and Kubernetes.
Experience integrating AI solutions with ERP, CRM, and enterprise business applications.
Background in product-based or ISV organizations.
Experience working in Agile environments with distributed teams.