New opportunity

AI Solution Architect

tarento · Bangalore, India

About this role

Tarento is seeking an AI Solution Architect in Bangalore for a full-time role. The posting lists 12–18 years of experience. The architect will design scalable services and architectures for enterprise-grade AI solutions and help shape AI strategy and adoption across an international business environment.

Responsibilities

Design and implement AI services and solution architectures; establish architecture standards, principles and best practices.
Embed security, privacy, compliance and ethical considerations into AI solutions.
Lead AI roadmap planning and implementation in line with business objectives. Work with business and technical stakeholders to turn requirements into technical solutions.
Provide architectural oversight of data pipelines, model development and deployment. Perform light coding and technical reviews to validate designs and implementations, and advise leadership and delivery teams on AI strategy and execution.

Qualifications

Strong Microsoft Azure experience and a proven background architecting AI/ML platforms; hands-on Python experience and prior work as a Data Scientist. Prior hands-on Data Scientist experience is mandatory.
Experience with algorithms, classical machine learning, model building, modern data stacks and AI model lifecycle management. Ability to define AI solution principles and explain how system components fit together.
Bachelor’s or Master’s degree in Mathematics, Statistics, Data Science or a related field. A minimum of 4 years in a similar AI Solution Architect or AI Engineering role is required; AI/ML specialization from 2017 onwards is preferred.

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

Skills for this role

AI solution architectureMicrosoft AzurePythonData scienceMachine learningAlgorithmsModel buildingModern data stacksAI model lifecycle managementData pipelinesModel deploymentSecurityPrivacyComplianceTechnical reviewsStakeholder collaboration

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

Scalable AI architecture
The role designs and implements enterprise-grade AI services and sets architecture principles and standards. Prepare to explain an Azure-based architecture, why its components fit together, and how it would scale.
Responsible AI controls
The posting requires security, privacy, compliance, and ethical considerations in AI solutions. Prepare to show where these concerns enter architecture decisions and how they affect implementation.
Business-aligned AI roadmap
The architect leads roadmap planning, translates stakeholder requirements into technical solutions, and advises leadership and delivery teams. Prepare an example of turning business objectives into sequenced architecture decisions.
End-to-end technical oversight
Oversight spans data pipelines, model development, deployment, and model lifecycle management. Prepare to explain how you would review an end-to-end design and validate key decisions through technical reviews or light coding.
Applied ML judgment
The posting calls for prior hands-on Data Scientist experience, Python, classical machine learning, algorithms, and model building. Prepare to explain how hands-on model work informs architecture choices rather than treating these as a separate skill list.

Likely challenges to think through

Standards without blocking delivery
A likely challenge is balancing reusable architecture standards with the needs of individual AI initiatives. Prepare a case showing which decisions you would standardize and where you would allow variation; this is a preparation hypothesis, not a reported Tarento issue.
Controls across the lifecycle
Security, privacy, compliance, and ethical considerations may introduce trade-offs in data access, model development, and deployment. Prepare to walk through those trade-offs at each stage; the posting does not specify a particular regulatory regime.
Strategy with technical credibility
The role combines roadmap leadership and stakeholder advice with light coding and technical reviews. A likely difficulty is staying sufficiently hands-on to validate designs while delegating delivery work. Prepare an example of how you would divide and connect those responsibilities.

Practice your answers

Technical practice

How would you turn a business objective into an enterprise AI solution architecture?

What to coverPreparation suggestion: Explain how you would clarify the objective, constraints and success measures with business and technical stakeholders before defining components and their interfaces. Discuss trade-offs between scalability, delivery effort and operational complexity.

How would you design a scalable AI/ML platform on Microsoft Azure?

What to coverPreparation suggestion: Describe how data ingestion, model development, deployment and monitoring would fit together on Azure, without assuming a particular service is required. Explain how you would evaluate architectural choices against scale, security and maintainability.

What architecture principles would you establish for AI solutions?

What to coverPreparation suggestion: Explain how principles and standards could guide component boundaries, reuse and technical reviews across delivery teams. Address when a justified exception might be preferable to rigid standardization.

How would you assess a data pipeline before approving it for model development?

What to coverPreparation suggestion: Cover data quality, lineage, access controls and the suitability of the pipeline for the intended modeling task. Explain how you would surface risks early and decide which issues must be resolved before deployment.

How would you choose and evaluate a classical machine learning approach?

What to coverPreparation suggestion: Compare a simple baseline with more complex algorithms using the business objective, data characteristics and appropriate evaluation measures. Discuss generalization, interpretability and the risk of misleading validation results.

What architectural oversight would you provide across the AI model lifecycle?

What to coverPreparation suggestion: Trace how a model moves from development through deployment and ongoing assessment, including what evidence you would request at handoffs. Explain trade-offs between faster releases and confidence in model behavior.

How would you embed security, privacy and compliance into an AI architecture?

What to coverPreparation suggestion: Describe how you would identify sensitive data and relevant obligations, then reflect them in access, data handling and review decisions. Distinguish controls that belong in the architecture from checks needed during implementation and operation.

How would you evaluate ethical risks in an AI solution?

What to coverPreparation suggestion: Explain how you would examine potential harms, affected users and limitations of the model before recommending safeguards. Discuss how you would communicate a material unresolved risk to stakeholders.

What would you check in a light Python code review of a model implementation?

What to coverPreparation suggestion: Focus on whether the code supports the intended design, including data handling, reproducibility, validation and maintainability. Explain when a small code check is enough and when deeper technical review is warranted.

How would you build an AI roadmap aligned with business objectives?

What to coverPreparation suggestion: Explain how you would assess candidate initiatives for value, feasibility, dependencies and risk before sequencing them. Describe what evidence would prompt you to revise the roadmap during implementation.

Behavioural practice

Tell me about a time you reconciled conflicting business and technical requirements for an AI solution.

What to coverPreparation suggestion: Use a real situation to explain the disagreement, the actions you took to clarify priorities and the resulting decision. State the outcome and any trade-off stakeholders accepted.

Describe a time you changed the priority or direction of an AI roadmap.

What to coverPreparation suggestion: Give the situation, what evidence or business objective drove your recommendation, and how you secured agreement. Explain the result without overstating what the change achieved.

Tell me about a technical review in which you challenged an AI design or implementation.

What to coverPreparation suggestion: Describe the design concern, how you tested or explained it, and the action the team took. Close with the result and how you maintained a constructive working relationship.

Describe a time you advised leadership on an AI opportunity or risk.

What to coverPreparation suggestion: Set out the decision leadership faced, how you made technical implications understandable, and what recommendation you made. Explain the outcome, including any uncertainty you communicated.

Tell me about working across teams or locations to deliver an AI initiative.

What to coverPreparation suggestion: Use a real example to show the coordination challenge, the actions you took to align contributors, and the result. If your example spans different business contexts, explain how you adapted communication or decisions.

Sharpen your knowledge

Topics to prepare

Azure AI/ML platform architecture
Revise how data pipelines, model development, deployment and AI services fit into a scalable architecture. Exercise: sketch a hypothetical Azure-based solution and explain each component’s role and the principles behind the design. The posting requires strong Azure experience and AI/ML platform architecture.
Classical machine learning and algorithms
Compare two classical ML approaches for a hypothetical prediction task, including how you would select and evaluate a model. This revisits the algorithms, classical ML and model-building experience specified in the posting.
Data stacks and model lifecycle
Trace a model from incoming data through development, deployment and subsequent review; identify where architectural oversight is needed. This connects the posting’s modern data stack and AI model lifecycle requirements.
AI architecture governance
For a hypothetical AI service, list design decisions that address security, privacy, compliance and ethics, then turn them into a short set of architecture principles. The role calls for these considerations to be embedded in AI solutions.
AI strategy and roadmap alignment
Practice explaining how a business objective becomes an AI capability, an implementation sequence and a decision for stakeholders. The posting calls for roadmap leadership and advice to business and technical teams.

Skills to revise

Python model-building practice
In Python, build and evaluate a small classical ML model, then explain your algorithm choice and limitations. Hands-on Python and prior Data Scientist work are explicit requirements.
Azure solution design
Draft a component diagram for a hypothetical Azure AI solution and walk through its data flow, deployment path and scalability choices. This practices the Azure architecture and component-level explanation requested in the posting.
Light coding and technical review
Review a short Python model or data-pipeline example: flag implementation risks, propose corrections and explain whether it matches the intended architecture. The role includes light coding and technical reviews.
Requirements-to-roadmap translation
Take a hypothetical business request, write clarifying questions, outline a technical solution and sequence its implementation decisions. This practices the stated stakeholder collaboration and AI roadmap responsibilities.

Your preparation checklist

Map your experience to the stated requirements
The posting specifies 12–18 years of experience, 4+ years in a similar architecture or AI engineering role, and mandatory hands-on Data Scientist experience. Prepare a dated evidence sheet showing relevant roles, responsibilities, and education.
Build an enterprise AI project walkthrough
The role involves scalable AI services and strong Microsoft Azure experience. Prepare one architecture diagram and a concise account of design decisions, component interactions, implementation, and trade-offs from a relevant project.
Show hands-on model-building evidence
The posting calls for Python, algorithms, classical machine learning, and model building. Select a Data Scientist example and be ready to explain your code contribution, model choice, evaluation, and limitations.
Practice an end-to-end architecture exercise
As preparation for the stated oversight of data pipelines, model development, deployment, and lifecycle management, sketch an AI platform design and explain how data, models, services, and operational controls fit together.
Prepare a governance and review example
The role calls for security, privacy, compliance, ethics, light coding, and technical reviews. Prepare a concrete example of how you identified risks, set architecture principles, and validated an implementation.
Draft a roadmap discussion and employer questions
The posting emphasizes business alignment, stakeholder collaboration, and advising leadership. Prepare an example of turning requirements into an AI roadmap, then ask how Tarento prioritizes use cases, defines architecture standards, and divides review responsibilities.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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