Date: 14/07/2026
Job Title: AI Architect
Location: Pune / Bangalore
Join us as a AI Architect within the AI Engineering Division of DAAS (Data, AI & Analytics Services), The role focuses on the design and implementation of enterprise-scale AI, GenAI, and Agentic AI solutions. The role will be responsible for defining AI architecture strategies, designing scalable cloud-native AI platforms on Azure, and enabling AI-powered business transformation across the organization.
Technical Skills
Expertise is required in the following fields
Lead the architecture and design of AI-powered solutions, including Copilots, Intelligent Assistants, Agentic AI systems, AI-enabled workflows, and business automation platforms.
Design scalable GenAI application architectures leveraging LLMs, Retrieval-Augmented Generation (RAG), agent frameworks, vector databases, and orchestration platforms.
Architect enterprise AI platforms using Azure OpenAI, Azure AI Services, Azure ML, Azure Databricks, Cognitive Search, and Microsoft 365 extensibility capabilities.
Establish AI reference architectures, design standards, reusable frameworks, and best practices for enterprise-wide AI adoption.
Good understanding of AI Gov Ops and AI Observability market landscape (such as solutions, frameworks and libraries) and architectural approaches to implementation of preventative, detective and corrective AI risk controls.
Partner with engineering teams to implement LLMOps, model lifecycle management, AI observability, evaluation frameworks, and deployment automation.
Drive API-first, event-driven, and cloud-native architectural patterns to support AI solution scalability and interoperability.
Provide technical leadership, architecture governance, and mentorship to AI engineering and solution delivery teams.
Collaborate closely with business, security, engineering, and data teams to identify, assess, and deliver high-value AI use cases.
What You will be Doing
Leading the design of enterprise solutions that embed LLMs, GenAI, and agentic automation into business workflows.
Ability to liaise with integration teams to support design and delivery end-to-end across structured and unstructured data sources and platforms, such as Databricks, Microsoft 365, SharePoint, Salesforce, Guidewire, ServiceNow, Workday, etc.
Providing technical leadership and guidance for pilot and solution delivery teams, building reference implementations and scaling successful patterns
Partnering with security, platform and engineering teams to enable LLMOps and AgenticOps capabilities – prompt lifecycle management, model observability, caching, evaluation and governance.
Contributing to the growth of an AI-ready architecture practice within AXA, including knowledge sharing and reference implementation development
Collaborating with business teams to identify and shape use cases for AI-powered automation, intelligent summarisation, triage, classification, and advisory systems
Supporting integration with messaging systems, event-driven architectures, and API ecosystems as part of AI solution delivery
Defining and evolving the enterprise GenAI and broader AI architecture roadmap aligned with AXA’s data and digital strategy.
Leading the architectural design of an enterprise AI Trust and Controls layer to manage AI Trust risks, such as Data risks (e.g. toxicity, sensitive data leakage), Prompting risks (e.g. prompt injection, jail breaking), Application and Security risks (e.g. insecure output handling, poisoned model usage), etc.
Innovating and reimagining business processes to take competitive advantage via the use of AI.
Soft Skills
Excellent communication skills to facilitate collaboration between business stakeholders and technical teams.
Strong analytical and problem-solving skills, with a focus on optimizing ML workflows.
Ability to work effectively in a global, multidisciplinary team environment.
Autonomy and innovation capability to advance MLOps practices within the organization.
Background and Experience
12+ years of hands-on experience on AI Engineering projects with a particular emphasis on leading architectural design of enterprise-scale GenAI and Agentic AI based solutions.
Experience with Cloud, preferably Azure tools and services specifically tailored for LLMOps.
Track record of delivering AI solutions from strategy through implementation and production adoption.