Description

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.

Preferred Qualifications

• 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.

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field

Key Skills

Education

Bachelor's or Master's degrees