New opportunity

Engineering Manager, AI Platforms & Architecture

Vanguard · Hyderabad, India

About this role

The Engineering Manager for AI Platforms & Architecture will lead a team of engineers and technical leads responsible for architecting, developing, and supporting enterprise technology solutions across multiple platforms. This role provides senior-level technical leadership, drives modern engineering practices, oversees secure API integrations, and ensures alignment between technology architecture, business priorities, and user needs. The position is based in Hyderabad, India, and operates under a hybrid working model.

Responsibilities

Lead, coach, and develop high-performing engineering teams by setting clear expectations, fostering career growth, and driving accountability for results.
Provide strategic technical leadership and architectural direction for complex, enterprise-scale software and platform solutions.
Drive the design, development, implementation, testing, deployment, and support of secure, scalable, and resilient technology solutions.
Review and approve technical designs, architectural recommendations, and engineering deliverables to ensure alignment with enterprise standards and business objectives.
Partner with business, product, UX, infrastructure, and security teams to deliver technology solutions that enhance user experience and operational efficiency.
Lead resolution of complex technical challenges through root-cause analysis, risk mitigation, and proactive problem prevention.
Establish and maintain engineering best practices, technical standards, governance processes, and software documentation frameworks.
Guide the adoption of modern software engineering methodologies, development tools, automation, and cloud-native technologies.
Drive API strategy and integration architecture to support secure and seamless data exchange across enterprise systems and applications.
Ensure solutions comply with information security, privacy, risk, and regulatory requirements.
Mentor engineers and technical leaders, fostering continuous learning and technical excellence across the organization.
Contribute to strategic technology roadmaps, architecture evolution, and long-term investment planning.
Identify opportunities to improve engineering productivity, software quality, development efficiency, and platform scalability.
Stay current on emerging technologies, industry trends, AI innovations, and modern software architecture practices.

Required qualifications

Bachelor’s degree in computer science, Software Engineering, Information Technology, or a related discipline.
Minimum 10 years of software engineering or technology experience with at least 5 years leading engineering teams and managers.
Demonstrated success delivering enterprise-scale applications and technology platforms from concept through production.
Strong expertise in software architecture, distributed systems, application engineering, and modern development practices.
Proficiency in one or more programming languages such as Java, Python, C#, or JavaScript.
Experience with Agile development methodologies, DevOps practices, CI/CD pipelines, and test automation.
Strong understanding of cloud platforms (AWS, Azure, or GCP), containerization, and microservices architecture.
Experience designing and integrating APIs and enterprise-scale software solutions.
Excellent analytical, problem-solving, stakeholder management, and communication skills.
Proven ability to build, lead, and develop high-performing engineering teams.

Preferred qualifications

Master's degree.
Experience leading multiple engineering teams, technical leaders, or platform organizations.
Strong understanding of Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern AI architectures.
Experience building and scaling enterprise AI products, platforms, or developer enablement capabilities.
Knowledge of AI governance, model risk management, Responsible AI, security, privacy, and compliance frameworks.
Experience with agentic AI, multi-model architectures, and enterprise automation solutions.
Ability to define technology vision, roadmaps, and investment priorities aligned to strategic business outcomes.
Experience leading globally distributed engineering organizations.
Financial services or other highly regulated industry experience.
Experience driving enterprise-wide technology transformation and adoption initiatives.

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

Skills for this role

Software ArchitectureDistributed SystemsJavaPythonC#JavaScriptAgileDevOpsCI/CDTest AutomationAWSAzureGCPContainerizationMicroservicesAPI IntegrationArtificial IntelligenceMachine LearningLarge Language Models (LLMs)Retrieval-Augmented Generation (RAG)AI GovernanceResponsible AIAgentic AI

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 Platforms
The role requires designing and scaling AI products and platforms that integrate with existing enterprise systems. You must demonstrate the ability to bridge the gap between complex AI/ML architectures (such as RAG or agentic AI) and the practical, secure, and resilient requirements of a large-scale financial services environment.
Leadership of High-Performing Engineering Teams
You are expected to lead, coach, and develop engineering teams and technical leads. This involves setting clear expectations, fostering career growth, and driving accountability for results while managing the complexities of a globally distributed organization.
Governance and Regulatory Compliance
Given the financial services context, you must ensure all AI and software solutions comply with strict information security, privacy, and risk management frameworks. You will be responsible for establishing technical standards and governance processes that align with enterprise-wide regulatory requirements.
Strategic Stakeholder Alignment
You must partner effectively with business, product, UX, infrastructure, and security teams. A key priority is translating business priorities into technical roadmaps and ensuring that engineering deliverables directly support the company's long-term investment and operational goals.

Likely challenges to think through

Balancing Innovation with Financial Regulation
Implementing cutting-edge AI technologies like LLMs or agentic AI in a highly regulated industry creates a tension between rapid innovation and strict compliance. Prepare by discussing how you have navigated model risk management, Responsible AI, and security constraints without stifling development velocity.
Managing Distributed Engineering Teams
Leading a team in a hybrid, globally distributed model requires intentional communication and culture-building. Prepare by highlighting your strategies for maintaining engineering excellence, mentorship, and team cohesion across different time zones and physical locations.
Integrating AI into Legacy Enterprise Ecosystems
Scaling AI platforms often involves complex API integrations with existing enterprise systems. The challenge lies in ensuring seamless data exchange and scalability while maintaining system resilience. Prepare to discuss your experience with API strategy and modernizing legacy architectures to support new AI capabilities.

Practice your answers

Technical practice

Architecting Enterprise-Scale AI Platforms

What to coverDiscuss your approach to designing scalable AI platforms that integrate with existing enterprise systems. Focus on how you balance performance, security, and maintainability while ensuring the architecture supports future growth and evolving AI models.

Implementing RAG and LLM Architectures

What to coverExplain the technical considerations for implementing Retrieval-Augmented Generation (RAG) within a secure enterprise environment. Address challenges such as data privacy, latency, and ensuring the accuracy of model outputs.

API Strategy and Secure Integration

What to coverDescribe your methodology for designing secure API integrations across complex, distributed systems. Highlight how you ensure data consistency and security compliance while maintaining seamless interoperability between legacy and modern platforms.

Cloud-Native Engineering and DevOps

What to coverDetail your experience with cloud-native technologies and CI/CD pipelines in a regulated environment. Discuss how you automate testing and deployment to improve software quality and engineering productivity.

AI Governance and Risk Management

What to coverExplain how you incorporate AI governance and model risk management into the development lifecycle. Suggestion: Focus on how you balance innovation with the need for compliance, security, and ethical AI practices.

Microservices and Distributed Systems

What to coverDiscuss the trade-offs you consider when migrating or building enterprise applications using microservices. Address how you manage service communication, data consistency, and operational complexity.

Agentic AI and Automation

What to coverDescribe your experience with agentic AI and multi-model architectures for enterprise automation. Explain how you design these systems to be resilient and how you handle error recovery in automated workflows.

Root-Cause Analysis and Problem Prevention

What to coverProvide an example of how you lead the resolution of complex technical challenges. Emphasize your process for conducting root-cause analysis and implementing proactive measures to prevent future occurrences.

Behavioural practice

Leading and Developing High-Performing Teams

What to coverDescribe a situation where you had to coach or mentor a team to improve their performance. Use the STAR method to explain the specific actions you took to foster growth and the resulting impact on team productivity.

Stakeholder Management and Alignment

What to coverShare an example of how you aligned technical architectural decisions with business priorities and user needs. Explain how you navigated conflicting requirements from product, security, or infrastructure teams.

Driving Organizational Change

What to coverDiscuss a time you led an enterprise-wide technology transformation or adopted a new engineering methodology. Detail the challenges you faced in gaining buy-in and how you measured the success of the initiative.

Managing Globally Distributed Teams

What to coverExplain your approach to leading and collaborating with globally distributed engineering organizations. Provide an example of how you maintained team cohesion, communication, and accountability across different time zones.

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Risk Management
Review frameworks for model risk management, data privacy, and security in highly regulated environments. Exercise: Draft a hypothetical governance checklist for deploying a RAG-based LLM application in a financial services context, focusing on compliance and bias mitigation.
Modern Software Architecture for AI Platforms
Study patterns for scalable, resilient, and cloud-native AI architectures, including microservices and multi-model integration. Exercise: Design a high-level architecture diagram for an enterprise-scale AI platform that supports both batch and real-time inference, ensuring secure API integration.

Skills to revise

Strategic Technical Leadership
Focus on defining technology roadmaps and aligning engineering deliverables with business outcomes. Exercise: Prepare a case study from your experience where you balanced technical debt with the need for rapid feature delivery in a large-scale environment.
Engineering Team Development
Practice coaching and mentoring techniques for high-performing, globally distributed teams. Exercise: Create a plan for fostering continuous learning and technical excellence within a team of engineers and technical leads.
Cloud-Native CI/CD and Automation
Refine knowledge of modern DevOps practices and automation for enterprise-scale deployments. Exercise: Outline a strategy to improve deployment frequency and software quality for a complex, distributed application using CI/CD pipelines.

Your preparation checklist

Map your leadership experience to enterprise-scale delivery
Prepare specific examples of leading engineering teams through the full lifecycle of enterprise-scale applications. Focus on how you managed technical debt, ensured architectural alignment with business goals, and mentored technical leads to drive productivity.
Develop an AI platform architecture case study
Create a brief walkthrough of an AI or ML platform you have architected or scaled. Highlight your approach to RAG, LLM integration, or agentic AI, and explain how you balanced innovation with security, privacy, and model risk management.
Prepare for discussions on AI governance and compliance
Review frameworks for Responsible AI and data privacy in highly regulated industries. Be ready to discuss how you implement governance processes to ensure AI solutions comply with security and regulatory requirements.
Refine your strategy for hybrid and global team management
Draft a summary of your approach to fostering a high-performing culture in a hybrid, globally distributed environment. Emphasize your methods for maintaining engineering standards, documentation frameworks, and continuous learning across teams.
Formulate strategic questions for the interviewers
Prepare insightful questions regarding Vanguard's current AI roadmap, how they prioritize technology investments, and the specific challenges they face in integrating AI platforms with existing legacy enterprise systems.

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