AI & Automation Engineer at Thomson Reuters in Bengaluru

Job description

Summary: The AI & Automation Engineer is an embedded practitioner, builder, and multiplier — sitting at the intersection of data engineering, AI systems, and operational workflows to assess, redesign, and transform complex manual processes into intelligent, automated operations. This role requires fluency in both AI and automation as distinct disciplines. You will assess and redesign existing operational processes before determining where deterministic automation, agentic AI, or a combination of both is the right solution — replacing manual, fragmented processes with streamlined, end-to-end intelligent systems that reduce friction, accelerate decisions, and embed intelligence directly into how work gets done. This is an embedded role operating within the Enterprise Analytics Center of Excellence, partnering closely with Data & Analytics team members, operations owners, and non-technical stakeholders to identify opportunities, design solutions, and drive adoption. You will build scalable systems that integrate data, AI, and user interaction — including natural language access to data and automated, push-based intelligence — embedding intelligence directly into how teams work. About the role: Assess and redesign existing operational processes to determine the right solution — deterministic automation, agentic AI, or a combination — before building Design and deploy event-driven, threshold-based intelligence systems that replace manual reporting Build AI-generated narrative and push communication systems that deliver personalized, context-rich insights and initiate or guide action within operational workflows Develop interactive feedback loops that allow recipients to respond to AI-surfaced signals (e.g., "this is expected," "I have questions," "investigate further") and route those responses appropriately Automate routine operational notifications (e.g., risk flag escalation) end-to-end with minimal human-in-the-loop dependency Integrate large language model (LLM) capabilities with semantic data models to enable non-technical users to directly interrogate data in natural language Partner with the team's semantic and architect subject area experts to ensure semantic models are AI-grade — well-defined, explainable, and optimized for NLQ intent Build and maintain prompt frameworks, evaluation approaches, and guardrails that ensure AI outputs are reliable, auditable, and aligned with business context Design and deliver robust, scalable reference implementations that serve as the foundation for broader adoption Define patterns and guardrails for building AI-enabled systems, enabling consistent and extensible approaches across the team Establish high-quality, maintainable solutions that are designed for reuse, durability, and evolution Partner with team members to share knowledge and evolve solutions into team-owned, repeatable capabilities Guide team members in designing and operating AI-enabled systems, helping shift from manual processes to automated, intelligent workflows Engage business leaders and operations owners to identify high-value automation and AI opportunities, define success criteria, and drive adoption of deployed solutions Contribute to the team’s technical direction by staying current on emerging AI and automation practices and bringing relevant innovations into the team’s approach About you: Demonstrated experience designing and deploying AI-powered systems used within operational workflows, including agentic workflows, orchestration, context engineering, and production-grade implementation with built-in evaluation and reliability Strong understanding of data foundations, including preparing data for AI use and working with semantic models and structured pipelines Experience integrating AI/LLM capabilities with structured data sources (e.g., semantic layers, data warehouses, APIs) Ability to design systems end-to-end, from data signal to AI reasoning to user-facing output Strong documentation practices, with a focu