New opportunity

Staff AI Solution Architect

GE Vernova · Bengaluru, India

About this role

The Staff AI Solution Architect will lead the design, development, and deployment of AI-driven enterprise applications and services to support data analytics across global functions including Finance, Treasury, and Sourcing. This role involves end-to-end ownership of AI components, from architecture and testing to documentation and deployment within large-scale data programs. The architect will utilize platforms such as Databricks, Microsoft Data Fabric, Microsoft AI Foundry, and AWS to build scalable, standardized solutions that deliver measurable value through the SQDC framework. Key Responsibilities: Strategic Collaboration: Engage in technical and business discussions to shape AI architecture. Architecture Planning: Develop implementation roadmaps and translate business needs into architectural requirements. Solution Development: Partner with service providers to build solutions aligned with enterprise standards. Compliance & Standardization: Ensure adherence to GDPR, SOX, and Responsible AI guidelines. Performance & Security Oversight: Monitor deployed solutions for optimal performance and security. Additional Responsibilities: Collaborate with cross-functional teams, including Data Architecture and Analytics, to execute roadmaps. Partner with data scientists and engineers to ensure robust implementations. Define and track KPIs to measure the impact of AI initiatives. Post-launch optimization through feedback and performance monitoring. Qualifications: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Proven experience designing and deploying cloud-based AI solutions. Expertise in Databricks and the Microsoft AI ecosystem. Familiarity with automation and DevOps practices. Strong business acumen with the ability to align technical solutions with strategic goals. Demonstrated leadership in managing cross-functional teams and complex projects. Experience with GE or GE Vernova is highly preferred. Relocation assistance is provided.

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

Skills for this role

DatabricksAWSMicrosoft Data FabricMicrosoft AI FoundryCloud-native AI toolsDevOpsAutomationArchitecture DesignGDPRSOXResponsible 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

Enterprise AI Architecture & Standardization
The role requires designing scalable, standardized AI solutions using Databricks, Microsoft Data Fabric, and AWS. You must prioritize creating architectures that are not only technically robust but also align with enterprise-wide governance and the SQDC framework to ensure consistency across global functions like Finance and Sourcing.
Strategic Business Alignment & KPI Tracking
You must translate complex business needs into architectural requirements and demonstrate measurable value. This involves defining and tracking specific KPIs to prove the impact of AI initiatives on business outcomes, requiring a strong ability to bridge the gap between technical implementation and strategic business goals.
Compliance and Responsible AI Governance
A critical priority is ensuring all AI deployments strictly adhere to GDPR, SOX, and Responsible AI guidelines. You will be responsible for the end-to-end oversight of security and compliance, ensuring that every solution developed meets rigorous corporate and regulatory standards.

Likely challenges to think through

Managing Multi-Cloud Complexity
The role requires expertise across both Microsoft (Data Fabric, AI Foundry) and AWS ecosystems. The challenge lies in maintaining a unified, standardized architecture while navigating the distinct capabilities and limitations of these different cloud platforms. Prepare by demonstrating how you have successfully integrated cross-platform services in previous enterprise environments.
Cross-Functional Stakeholder Alignment
You will lead initiatives involving data scientists, engineers, and product managers while partnering with external service providers. The difficulty is maintaining technical integrity and project momentum across diverse teams with varying priorities. Prepare by highlighting your experience in managing complex, cross-functional projects and your approach to resolving technical disagreements while maintaining alignment with the product roadmap.

Practice your answers

Technical practice

Designing Scalable AI Architectures on Databricks

What to coverExplain how you would design a scalable AI architecture using Databricks within a multi-cloud environment like Microsoft Data Fabric and AWS. Focus on data processing efficiency, model serving, and how you ensure the architecture remains standardized across different global functions.

Integrating Responsible AI and Compliance

What to coverDescribe your approach to embedding Responsible AI guidelines, GDPR, and SOX compliance into the AI development lifecycle. Discuss how you balance innovation with strict regulatory requirements in an enterprise setting.

AI Solution Deployment and DevOps

What to coverHow do you incorporate DevOps practices and automation into the deployment of AI-driven applications? Address how you manage CI/CD pipelines for machine learning models to ensure consistent performance and security.

Measuring AI Impact via KPIs

What to coverThe role requires delivering measurable value through the SQDC framework. Provide examples of how you define and track KPIs for AI initiatives to demonstrate business impact and operational excellence.

Cross-Platform AI Integration

What to coverDiscuss the challenges and strategies for building AI solutions that leverage both Microsoft AI Foundry and AWS. Focus on interoperability, data governance, and maintaining a unified architectural standard.

Post-Launch Optimization and Monitoring

What to coverExplain your methodology for monitoring deployed AI solutions for performance and security. How do you use feedback loops and performance metrics to iterate on features and enhance value delivery post-launch?

Translating Business Needs to Architecture

What to coverDescribe your process for translating complex business requirements from departments like Finance or Sourcing into technical architectural roadmaps. How do you ensure the technical design directly supports strategic business goals?

Managing Large-Scale Data Programs

What to coverWhat are the critical considerations when scaling AI applications from pilot to enterprise-wide deployment? Address data architecture, infrastructure governance, and managing technical debt.

Behavioural practice

Leading Cross-Functional Collaboration

What to coverDescribe a situation where you had to lead a cross-functional team including data scientists, engineers, and product managers to execute a complex AI project. How did you align their different perspectives to achieve a successful outcome?

Managing Stakeholder Expectations

What to coverProvide an example of how you engaged with business stakeholders to shape an AI product roadmap. How did you handle conflicting priorities or technical constraints while maintaining alignment with strategic objectives?

Proactive Risk Management

What to coverTell me about a time you identified a significant technical or compliance risk in an AI project before it became a problem. What actions did you take to mitigate the risk and ensure the project stayed on track?

Driving Continuous Improvement

What to coverGive an example of how you identified an opportunity to improve an existing AI solution or process. What steps did you take to implement the change, and how did you measure the resulting improvement?

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Compliance
Review the requirements for GDPR and SOX compliance within AI architectures. Exercise: Map out a data lineage and access control strategy for a hypothetical financial AI model to ensure it meets strict regulatory standards for data privacy and financial reporting.
SQDC Framework Alignment
Understand the Safety, Quality, Delivery, and Cost (SQDC) framework in the context of industrial AI. Exercise: Define specific KPIs for an AI-driven sourcing application that directly correlate to cost reduction and delivery efficiency, demonstrating how you would track these metrics post-deployment.
Hybrid Cloud AI Architecture
Study the integration patterns between Microsoft AI Foundry, Microsoft Data Fabric, and AWS. Exercise: Design a high-level architecture diagram that shows how data flows from an AWS-based source into a Microsoft-based AI service, highlighting where standardization and scalability are maintained.

Skills to revise

Databricks Solution Design
Focus on building scalable AI pipelines using Databricks. Exercise: Create a mock implementation plan for a large-scale data analytics program that utilizes Databricks for model training and deployment, ensuring the solution is modular and reusable across different business functions.
DevOps for AI (MLOps)
Practice automating the deployment and monitoring of AI models. Exercise: Draft a CI/CD pipeline strategy for an AI service that includes automated testing, performance monitoring, and a feedback loop for post-launch optimization as required by the role.

Your preparation checklist

Map AI Architecture to Business KPIs
Prepare a summary of how you have previously aligned AI initiatives with business outcomes, specifically using frameworks like SQDC (Safety, Quality, Delivery, Cost). Be ready to discuss how you define and track KPIs for enterprise-scale AI projects.
Demonstrate Cloud-Native AI Expertise
Prepare a technical walkthrough of a project where you deployed AI solutions using Databricks and the Microsoft AI ecosystem. Highlight your experience with AWS integration and how you managed the end-to-end lifecycle from architecture to deployment.
Address Compliance and Responsible AI
Develop a brief case study on how you have implemented Responsible AI guidelines, GDPR, and SOX compliance within a large-scale data analytics environment. Explain your approach to balancing innovation with enterprise security and governance standards.
Prepare for Cross-Functional Leadership Discussions
Reflect on your experience leading cross-functional teams, including data scientists and engineers. Prepare examples of how you have translated complex business needs into actionable architectural roadmaps and managed stakeholder expectations.
Formulate Strategic Questions for the Interviewer
Prepare thoughtful questions regarding the current maturity of the AI infrastructure at GE Vernova, the specific challenges in scaling AI for Finance and Sourcing functions, and how the team currently balances service provider partnerships with internal development.

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…