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Staff Software Engineer - AI I

Thomson Reuters · Bengaluru, India

About this role

Role Purpose: As a Staff Software Engineer – AI, you will spearhead the development of cutting-edge AI-powered solutions and drive technical strategy at Thomson Reuters. You will collaborate with senior stakeholders, product leadership, and cross-functional teams to architect, build, and scale enterprise-grade AI systems that transform professional work across legal, tax, risk, and compliance domains. Responsibilities: Design and lead the implementation of complex, enterprise-scale AI systems, including multi-component pipelines, advanced RAG architectures, and autonomous AI agents with multi-step reasoning. Architect scalable AI model integration strategies using APIs, distributed data pipelines, and microservices. Lead the adaptation and customization of AI models for specialized domains. Define technical standards, best practices, and governance frameworks for AI system architecture and lifecycle management. Mentor senior and junior engineers, champion MLOps/LLMOps practices, and serve as a senior thought leader in executive forums. Design testing frameworks, monitoring systems, and observability solutions for AI performance at scale. Qualifications: Bachelor's degree in Computer Science, Engineering, or equivalent (Master's preferred). Minimum 8 years of experience in software engineering, with at least 4 years in senior or lead roles focused on AI/ML. Expert-level proficiency in Python and AI/ML frameworks (PyTorch, TensorFlow, JAX). Deep understanding of machine learning principles, distributed system design, and cloud-native architectures (AWS, Azure, GCP). Proven experience with MLOps/LLMOps, orchestration tools (Docker, Kubernetes, Terraform), and integrating AI models into large-scale production systems. Advanced knowledge of vector databases, embeddings, semantic search, and information retrieval. Strong leadership, strategic thinking, and communication skills. Domain expertise in legal, tax, or accounting is highly valued. Benefits: Hybrid work model (2-3 days in office), flexible work arrangements, career development programs, comprehensive benefits including flexible vacation, mental health support, retirement savings, and tuition reimbursement.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonPyTorchTensorFlowJAXMLOpsLLMOpsAWSAzureGCPDockerKubernetesTerraformRAGAI AgentsVector databasesEmbeddingsSemantic searchInformation retrieval

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Architecting Enterprise-Scale AI Systems
The role requires designing and implementing complex, multi-component pipelines, advanced RAG architectures, and autonomous AI agents. You must demonstrate the ability to scale these systems for production environments while maintaining high performance and reliability.
Establishing MLOps and LLMOps Standards
You are responsible for defining technical standards, best practices, and governance frameworks for the entire AI lifecycle. This includes designing observability solutions, monitoring systems, and testing frameworks to ensure AI performance at scale.
Strategic Technical Leadership and Mentorship
As a senior thought leader, you must influence product strategy, drive technical roadmaps, and mentor engineering teams. You will be expected to communicate complex AI concepts to senior stakeholders and represent the organization in executive forums.
Domain-Specific AI Customization
You must lead the adaptation and customization of AI models for specialized domains such as legal, tax, and compliance. This requires balancing model performance with strict adherence to ethical AI principles and regulatory requirements.

Likely challenges to think through

Balancing Innovation with Regulatory Compliance
Operating in highly regulated fields like legal and tax means that cutting-edge AI features must meet rigorous security and ethical standards. Prepare by discussing your experience with model risk management and how you have navigated compliance constraints without stifling technical innovation.
Scaling AI Systems in Complex Environments
Transitioning from proof-of-concept to enterprise-grade production systems involves significant technical hurdles regarding latency, cost, and resource usage. Be ready to discuss specific strategies for optimizing distributed data pipelines and microservices architectures in a cloud-native environment.
Driving Consensus Across Cross-Functional Teams
The role requires partnering with AI researchers, product leadership, and engineering teams to translate capabilities into market-leading applications. Prepare by highlighting your experience in influencing stakeholders who may have competing priorities or different technical perspectives.

Practice your answers

Technical practice

Architecting Enterprise RAG Systems

What to coverDiscuss the challenges of scaling Retrieval-Augmented Generation (RAG) in a production environment. Focus on strategies for managing vector database latency, ensuring high-quality retrieval through advanced embedding techniques, and maintaining data privacy in regulated domains like legal or tax.

Implementing Autonomous AI Agents

What to coverExplain your approach to designing agents capable of multi-step reasoning. Address how you handle error propagation in long-running chains, state management, and the trade-offs between using pre-built agent frameworks versus custom-built orchestration logic.

LLMOps and Lifecycle Governance

What to coverDescribe how you implement LLMOps to manage the full lifecycle of AI models. Focus on versioning, automated evaluation frameworks for model performance, and establishing governance to ensure compliance with ethical AI and security standards.

Distributed System Design for AI

What to coverHow do you architect distributed data pipelines to support high-throughput AI model inference? Discuss the role of microservices, container orchestration (Kubernetes), and cloud-native infrastructure in maintaining system reliability and cost-efficiency.

Observability and Testing for AI

What to coverWhat are the key metrics you monitor for AI systems in production beyond standard latency and throughput? Suggest frameworks for detecting model drift, hallucinations, and performance degradation in specialized domain applications.

Model Adaptation and Customization

What to coverWhen adapting LLMs for specialized domains like legal or accounting, what strategies do you use to balance fine-tuning versus prompt engineering? Discuss the trade-offs regarding resource usage, model accuracy, and maintenance overhead.

Cloud-Native AI Infrastructure

What to coverCompare the trade-offs of deploying AI models on AWS, Azure, or GCP. Address how you leverage specific cloud-native tools for infrastructure as code (Terraform) and managing GPU resources for large-scale training or inference.

Information Retrieval at Scale

What to coverDiscuss the evolution of semantic search and its role in enterprise AI. Explain how you optimize vector databases for large datasets and the impact of different embedding models on retrieval precision.

Behavioural practice

Leading Technical Strategy

What to coverDescribe a situation where you had to define a technical standard or architecture for a complex AI initiative. How did you align senior stakeholders and cross-functional teams to ensure the strategy was adopted and scalable?

Mentorship and Team Growth

What to coverProvide an example of how you have mentored junior or senior engineers in the AI/ML space. How do you balance hands-on technical leadership with the need to foster the career development of your team members?

Influencing Product Direction

What to coverTell me about a time you used your technical expertise to influence product strategy. How did you translate complex AI capabilities into a compelling business case that addressed customer needs in a regulated industry?

Navigating Ambiguity and Fast-Paced Environments

What to coverDescribe a project where you had to lead a proof-of-concept for an emerging AI technology. How did you manage technical feasibility assessments while maintaining momentum in a fast-paced, enterprise environment?

Managing AI Governance and Ethics

What to coverShare an experience where you had to balance innovation with strict regulatory or ethical compliance requirements. How did you ensure the AI system remained compliant without stifling the development of the solution?

Sharpen your knowledge

Topics to prepare

Advanced RAG Architectures
Review concepts for multi-component retrieval pipelines, including hybrid search, reranking strategies, and query expansion. This is critical for the role's requirement to build enterprise-grade AI systems for legal and tax domains where accuracy and information retrieval are paramount.
Autonomous AI Agents and Multi-step Reasoning
Study frameworks for agentic workflows, including planning, tool use, and chain-of-thought reasoning. This aligns with the job's focus on developing autonomous agents capable of performing complex tasks in regulated industries.
AI Governance and Lifecycle Management
Examine frameworks for AI model risk management, ethical AI principles, and regulatory compliance. This is essential for the role's responsibility to define governance standards and ensure adherence to security and ethical requirements in enterprise settings.

Skills to revise

LLMOps and MLOps Implementation
Practice designing observability and monitoring solutions for AI models in production, including tracking latency, drift, and cost. This directly supports the requirement to champion MLOps/LLMOps practices and maintain AI performance at scale.
Vector Database Integration
Gain hands-on experience with vector databases and embedding management for large-scale semantic search. This is a core technical requirement for building the sophisticated information retrieval systems mentioned in the job description.
Cloud-Native Orchestration
Refine skills in deploying and managing AI services using Docker, Kubernetes, and Terraform on major cloud platforms. This is necessary for the role's focus on architecting scalable AI model integration strategies and distributed data pipelines.

Your preparation checklist

Prepare an architectural case study on RAG systems
Develop a detailed walkthrough of an enterprise-scale RAG system you have built. Focus on your choices for vector databases, embedding strategies, and how you managed latency and accuracy in a production environment. Be prepared to discuss how you handled multi-step reasoning and autonomous agent orchestration.
Document your MLOps and LLMOps lifecycle experience
Create a summary of your experience implementing CI/CD for AI models, including observability, monitoring, and testing frameworks. Highlight how you have managed model governance, security, and compliance, as these are critical for the legal and tax domains Thomson Reuters serves.
Formulate strategic questions for leadership
Prepare thoughtful questions regarding the company's current AI governance frameworks, how they balance innovation with regulatory compliance in the legal/tax sectors, and how the engineering team is currently scaling LLM-based solutions to meet enterprise-grade performance requirements.
Review your experience with cloud-native AI infrastructure
Be ready to discuss specific technical trade-offs you have made when deploying AI models on AWS, Azure, or GCP. Focus on your use of orchestration tools like Kubernetes and Terraform to manage distributed data pipelines and ensure cost-efficient, scalable AI operations.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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