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. 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 and fostering career growth.
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.
Partner with business, product, UX, infrastructure, and security teams to deliver technology solutions that enhance user experience.
Lead resolution of complex technical challenges through root-cause analysis and risk mitigation.
Establish and maintain engineering best practices, technical standards, 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 data exchange.
Ensure solutions comply with information security, privacy, risk, and regulatory requirements.
Contribute to strategic technology roadmaps, architecture evolution, and long-term investment planning.
Stay current on emerging technologies, industry trends, and AI innovations.

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.

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.
Experience leading globally distributed engineering organizations.
Financial services or other highly regulated industry experience.

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/CDCloud PlatformsAWSAzureGCPContainerizationMicroservicesAPI 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

Strategic AI Architecture Leadership
You must define and execute the technical roadmap for AI platforms, ensuring that architectural decisions align with enterprise-scale requirements. This requires balancing innovation in areas like LLMs and RAG with the need for stable, scalable, and secure production-grade systems.
Engineering Excellence and Governance
You are responsible for establishing and enforcing engineering standards, documentation frameworks, and AI governance. In a highly regulated financial services environment, you must ensure all AI solutions comply with strict security, privacy, and risk management protocols.
Team Development and Mentorship
A core priority is leading, coaching, and developing high-performing engineering teams. You must foster a culture of continuous learning, set clear performance expectations, and drive accountability while managing technical leads and potentially distributed teams.
Cross-Functional Stakeholder Alignment
You must partner effectively with business, product, UX, infrastructure, and security teams. Success depends on your ability to translate complex technical AI capabilities into solutions that meet specific business outcomes and enhance the user experience.

Likely challenges to think through

Balancing Innovation with Regulatory Rigor
Vanguard operates in a highly regulated industry where AI experimentation must be tempered by strict compliance and risk frameworks. Prepare to discuss how you manage the trade-off between rapid AI adoption and the necessity of model risk management and security.
Scaling AI Platforms in a Legacy Environment
Integrating modern AI architectures (like agentic AI or multi-model systems) into an established enterprise ecosystem often creates technical debt and integration friction. Prepare to explain your approach to modernizing legacy infrastructure while deploying new AI capabilities.
Managing Distributed Engineering Teams
Leading globally distributed teams requires intentional communication and process synchronization. Prepare to share your strategies for maintaining engineering quality, team cohesion, and consistent delivery standards across different time zones and working models.

Practice your answers

Technical practice

Architecting Enterprise AI Solutions

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 when implementing RAG or LLM-based architectures.

API Strategy and Integration

What to coverExplain your methodology for designing secure, resilient API integrations in a large-scale environment. Address how you manage versioning, authentication, and data exchange protocols to ensure seamless interoperability between microservices.

Cloud-Native Platform Engineering

What to coverDescribe your experience with cloud platforms (AWS, Azure, or GCP) and containerization strategies. Focus on how you optimize infrastructure for AI workloads, including considerations for cost, scalability, and resource management.

AI Governance and Responsible AI

What to coverHow do you incorporate AI governance, model risk management, and security into the development lifecycle? Suggestion: Discuss frameworks for ensuring compliance with privacy and regulatory requirements in a financial services context.

CI/CD and DevOps for AI

What to coverDetail your experience implementing CI/CD pipelines for AI/ML models. Explain how you handle automated testing, model deployment, and monitoring to ensure reliability and performance in production environments.

Managing Distributed Systems

What to coverWhat are the primary challenges you have faced when building and maintaining distributed systems? Focus on how you ensure data consistency, fault tolerance, and low latency across global architectures.

Technical Debt and Modernization

What to coverHow do you evaluate when to refactor legacy systems versus adopting new technologies? Suggestion: Describe your process for aligning technical modernization roadmaps with business priorities and long-term investment planning.

Root-Cause Analysis and Troubleshooting

What to coverDescribe a time you led the resolution of a complex technical failure in a production environment. Explain your process for root-cause analysis and the steps you took to implement proactive risk mitigation.

Behavioural practice

Leading High-Performing Engineering Teams

What to coverProvide an example of how you have coached and developed engineering talent to achieve high performance. Focus on how you set expectations, foster career growth, and drive accountability within your team.

Stakeholder Management and Alignment

What to coverDescribe a situation where you had to align technical architectural decisions with conflicting business or product requirements. How did you communicate the trade-offs to stakeholders to reach a consensus?

Driving Organizational Change

What to coverGive an example of a time you led an enterprise-wide technology transformation or the adoption of a new engineering methodology. How did you manage resistance and ensure successful implementation across multiple teams?

Managing Globally Distributed Organizations

What to coverWhat strategies have you used to maintain collaboration and technical excellence when leading teams across different geographic locations? Suggestion: Highlight your approach to communication, cultural inclusion, and standardizing engineering practices.

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Risk Management
Review frameworks for model risk management, data privacy, and security in financial services. Exercise: Draft a hypothetical governance checklist for deploying an LLM-based application, focusing on compliance, bias mitigation, and auditability to align with the role's emphasis on regulated industry standards.
Scalable AI Platform Architecture
Study patterns for building enterprise-grade AI platforms, including RAG pipelines and agentic AI architectures. Exercise: Design a high-level architecture diagram for a scalable RAG system that integrates with existing enterprise data, focusing on security, latency, and modularity.

Skills to revise

Strategic Technical Leadership
Practice articulating how technical roadmaps align with business outcomes. Exercise: Prepare a narrative on how you have previously balanced short-term delivery requirements with long-term architectural evolution, specifically in a cloud-native, distributed systems environment.
Stakeholder Management in Regulated Environments
Develop communication strategies for bridging technical and non-technical stakeholders. Exercise: Simulate a scenario where you must explain the risks and benefits of an AI implementation to a non-technical business partner, ensuring alignment with security and compliance requirements.
CI/CD and DevOps for AI/ML
Focus on MLOps practices, including automated testing and deployment for AI models. Exercise: Outline a CI/CD pipeline strategy for an AI service that ensures automated quality checks, security scanning, and seamless integration into production environments.

Your preparation checklist

Prepare an enterprise-scale architecture case study
Develop a structured walkthrough of a past project where you led the design and delivery of a complex, distributed system. Focus on your decision-making process regarding scalability, cloud-native technologies, and how you ensured the solution met business requirements.
Document your AI and platform leadership experience
Create a summary of your experience with AI/ML, LLMs, or RAG architectures. Highlight specific instances where you managed AI governance, model risk, or security frameworks within a highly regulated environment.
Refine your engineering management philosophy
Prepare concrete examples of how you have coached technical leads, managed globally distributed teams, and fostered career growth. Be ready to discuss your approach to setting engineering standards and driving adoption of modern DevOps and CI/CD practices.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding Vanguard's current AI roadmap, how the team balances innovation with regulatory compliance in financial services, and how the platform organization collaborates with product and security stakeholders.
Review API and integration strategy frameworks
Prepare to discuss your experience in designing secure API ecosystems. Be ready to explain how you have previously balanced the need for seamless data exchange with strict enterprise security and privacy requirements.

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…