New opportunity

Artificial Intelligence Architect

BlitzenX · Chennai, India

About this role

BlitzenX seeks an Artificial Intelligence Architect in Chennai to build AI capabilities and products from the ground up. The role owns AI architecture and product delivery from use-case discovery through production and scaling, working with executive leadership, product managers and engineering heads to deliver measurable business outcomes.

Responsibilities

Define end-to-end architecture covering data ingestion, model development, training, deployment, monitoring and improvement. Build scalable, secure and cost-efficient AI platforms, and establish reference architectures, design standards and technical guardrails.
Partner with business and product leaders to identify use cases tied to revenue, efficiency or customer impact. Translate ambiguous problems into roadmaps covering data strategy, model selection, MVPs, rollout, risks, milestones and success metrics; drive predictable delivery and iterative optimization.
Build and lead cross-functional AI teams, define roles and ownership, set performance expectations, and mentor senior engineers and architects.
Establish AI development lifecycle processes, including MLOps, CI/CD and model governance. Set standards for model versioning, explainability, bias detection, security, compliance and monitoring. Serve as the final technical authority on AI decisions while balancing delivery speed with production stability.
Communicate strategy, architecture and progress to senior leaders and clients, and support pre-sales and AI solutioning.

Requirements

10+ years of overall experience, including 5+ years in AI/ML architecture or leadership roles; proven experience building AI products from zero and leading and scaling cross-functional teams. Requires hands-on machine learning and deep learning expertise, with deep knowledge of at least one of NLP, computer vision or predictive modeling. Python is mandatory. Candidates need deep expertise in AWS, Azure or GCP for AI workloads; familiarity with TensorFlow, PyTorch or similar frameworks; and strong knowledge of MLOps, model deployment and monitoring, data lakes, feature stores, pipelines, APIs, microservices and production integration. The role calls for architectural judgment under ambiguity and an understanding of performance, cost and scalability trade-offs.

The posting lists the position as full-time and displays compensation as INR 1 - 2500000, without specifying a pay period.

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

Skills for this role

AI architectureMachine LearningDeep LearningNatural Language ProcessingComputer VisionPredictive ModelingMLOpsCI/CDModel deploymentModel monitoringModel versioningExplainabilityBias detectionAWSAzureGoogle Cloud PlatformPythonTensorFlowPyTorchData architectureData lakesFeature storesData pipelinesAPIsMicroservicesSecurity and complianceCost optimizationTeam leadershipProduct roadmappingStakeholder communication

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

Own production AI architecture
Define the architecture from data ingestion and model development through deployment, monitoring and improvement. The posting emphasizes scalable, secure, cost-efficient platforms and reference standards—not prototypes.
Turn use cases into delivered products
Work with business and product leaders to select use cases tied to revenue, efficiency or customer impact, then own roadmaps from data strategy and MVP through rollout, scaling and measurable outcomes.
Build and lead the AI organization
Build cross-functional teams from the ground up, establish roles and ownership, set performance expectations, and mentor senior engineers and architects.
Establish disciplined AI operations
Set the development lifecycle for MLOps, CI/CD and model governance, including versioning, explainability, bias detection, security, compliance and monitoring. The architect is the final technical authority on AI decisions.
Influence leaders and clients
Communicate strategy, architectural decisions and progress to senior leadership and clients, and support pre-sales solutioning where AI is a differentiator.

Likely challenges to think through

Choosing a viable starting point
Preparation hypothesis: Ambiguous business requests may compete for attention before data readiness or value is clear. Prepare a method for comparing use cases, defining an MVP and agreeing on measurable success without promising more than the data can support.
Balancing speed with production control
Preparation hypothesis: Pressure to ship could conflict with reliability, security and model governance. Prepare to explain which checks belong before launch, how monitoring and rollback would work, and how to make risks visible to decision-makers.
Managing scale, performance and cost
Preparation hypothesis: An architecture that works for an MVP may become too costly or slow as usage grows. Prepare to discuss cloud and model-selection trade-offs, capacity assumptions, and the signals that would trigger architectural changes.
Building a team while delivering
Preparation hypothesis: Establishing roles and standards from zero could compete with near-term product milestones. Prepare a phased ownership model that lets a cross-functional team deliver early work while developing repeatable practices.

Practice your answers

Technical practice

How would you architect an AI product from data ingestion through deployment and continuous improvement?

What to coverPreparation suggestion: Explain the responsibilities and boundaries of each component, including training, serving and monitoring. Discuss how you would use a cloud platform to meet the posting’s security, scalability and cost goals.

How would you design data pipelines, a data lake and a feature store for a new AI product?

What to coverPreparation suggestion: Address data quality, lineage and how training and production systems would use consistent data. Explain when a feature store adds value rather than treating it as a default component.

How would you choose and build a model for a use case in your strongest AI specialty?

What to coverPreparation suggestion: Use NLP, computer vision or predictive modeling, as relevant to your expertise, to compare a simple baseline with a more complex ML or deep-learning approach. Explain how Python and a framework such as TensorFlow or PyTorch would support implementation and evaluation.

How would you turn an ambiguous business problem into a testable AI MVP?

What to coverPreparation suggestion: Describe use-case discovery, data feasibility, model-selection criteria and a limited rollout. Tie success metrics to the posting’s stated outcomes of revenue, efficiency or customer impact, and identify risks that could change the roadmap.

What MLOps and CI/CD controls would you establish before an AI product goes to production?

What to coverPreparation suggestion: Cover reproducible training, model versioning, testing, release approvals and rollback. Explain how those controls support predictable delivery without allowing uncontrolled production experimentation.

How would you detect and respond when a deployed model’s performance deteriorates?

What to coverPreparation suggestion: Distinguish system health from model-quality monitoring and explain how you would investigate changes in data or outcomes. Describe criteria for retraining, rollback or another improvement, rather than assuming every alert needs a new model.

How would you set technical guardrails for explainability, bias, security and compliance?

What to coverPreparation suggestion: Describe how requirements would shape design reviews, evaluation and production monitoring. Explain trade-offs between safeguards, delivery speed and the level of risk in a use case without assuming a specific regulatory regime.

How would you integrate a model with an existing production system?

What to coverPreparation suggestion: Discuss API or microservice boundaries, deployment choices and operational failure handling. Explain how integration decisions affect reliability, latency and the ability to update a model safely.

How would you evaluate performance, cost and scalability trade-offs in an AI platform?

What to coverPreparation suggestion: Compare plausible training and serving options against workload needs and measurable constraints. Explain what you would measure before scaling and when a less complex architecture would be preferable.

How would you establish reference architectures without constraining every AI use case to one design?

What to coverPreparation suggestion: Identify which standards should be consistent across teams, such as security, deployment and monitoring, and where variation is justified. Describe how you would review exceptions and keep the standards useful as products scale.

Behavioural practice

Tell me about a time you turned an ambiguous AI opportunity into a deliverable roadmap.

What to coverPreparation suggestion: Give the situation, the actions you took to clarify the business outcome and sequence milestones, and the result. Include how you handled uncertainty or revised the plan when evidence changed.

Describe a time you built or scaled a cross-functional AI team.

What to coverPreparation suggestion: Explain the situation, how you defined roles and ownership or mentored senior contributors, and the result. Focus on observable effects on delivery and team stability rather than broad leadership claims.

Tell me about a technical decision where delivery speed conflicted with production stability.

What to coverPreparation suggestion: Set out the situation, the risks and options you weighed, the decision you made, and the result. Explain how you maintained accountability for a production-grade outcome.

Describe a time you communicated a consequential AI architecture decision to senior leaders or clients.

What to coverPreparation suggestion: Give the situation, how you explained trade-offs and evidence to that audience, and the resulting decision or next step. Be clear about what you knew, what remained uncertain and how you handled questions.

Tell me about a time product, engineering and AI stakeholders disagreed on priorities.

What to coverPreparation suggestion: Describe the situation, your actions to align priorities with measurable outcomes and ownership, and the result. Include how you kept delivery moving without overlooking a material concern.

Sharpen your knowledge

Topics to prepare

End-to-end AI architecture
Review how data ingestion, training, deployment, monitoring and improvement fit together. Preparation exercise: sketch a reference architecture and explain its security, scalability and cost trade-offs. The posting assigns ownership of this full lifecycle.
AI product roadmapping
Review how to turn an ambiguous business problem into a use case, data strategy, MVP, rollout plan and measurable outcome. Preparation exercise: outline milestones, risks and success metrics for a hypothetical use case. The role owns delivery from discovery to production.
ML and deep-learning design choices
Review model-selection and evaluation trade-offs, focusing deeply on at least one of NLP, computer vision or predictive modeling. Preparation exercise: compare two plausible approaches for a use case and justify a production choice. The posting explicitly requires hands-on ML and deep-learning expertise.
MLOps and model governance
Review CI/CD, model versioning, explainability, bias detection, security, compliance and monitoring. Preparation exercise: map the checks and decision points between training and a production release. The architect sets these lifecycle standards and balances speed with stability.
Technical leadership and decision-making
Review ownership models for data scientists, ML engineers, platform engineers and AI product contributors. Preparation exercise: explain how you would resolve an architecture disagreement and communicate the decision to senior leaders. The role builds teams and serves as final technical authority.

Skills to revise

Python model implementation
Preparation exercise: implement a small training and evaluation workflow in Python using TensorFlow or PyTorch, then explain its limits. Python is mandatory, and the posting expects experience with modern ML frameworks.
Cloud AI workload design
Preparation exercise: design a deployable AI workload on one of AWS, Azure or GCP and estimate where performance, cost and scaling pressures arise. The posting requires deep expertise in a cloud platform for AI workloads; the design exercise is a preparation suggestion.
Production data flow
Preparation exercise: specify a data pipeline from ingestion through a data lake and feature store to model training, including data-quality checks. The posting calls for strong data architecture fundamentals across those components.
Deployment and monitoring workflow
Preparation exercise: outline how to expose a model through an API, integrate it with a production service, version its releases and monitor its behavior after deployment. The posting names APIs, microservices, model deployment, monitoring and continuous improvement.

Your preparation checklist

Map evidence to the requirements
Prepare a one-page index of specific examples covering 10+ years overall, 5+ years in AI/ML architecture or leadership, Python, cloud AI workloads, ML frameworks, and deep expertise in at least one of NLP, computer vision, or predictive modeling. The posting names these requirements; do not claim experience you cannot substantiate.
Rehearse a zero-to-scale product walkthrough
Prepare one project account showing your decisions from use-case discovery through MVP, production rollout, monitoring, and improvement. Bring a diagram, your ownership boundaries, measurable business outcomes, and lessons from setbacks; the role emphasizes building AI products from scratch.
Sketch a production AI architecture
As interview preparation, draw a reference architecture covering ingestion, data lakes or feature stores, training, deployment, APIs or microservices, and monitoring. Explain cloud choices and performance, security, scalability, and cost trade-offs rather than assuming BlitzenX uses a particular stack.
Draft a use-case-to-roadmap exercise
Practice turning an ambiguous business goal into a proposed data strategy, model choice, MVP, milestones, risks, rollout, and success metrics tied to revenue, efficiency, or customer impact. Label the scenario hypothetical; the posting does not specify a product use case.
Prepare an operating-model example
Outline how you would set standards for MLOps, CI/CD, model versioning, explainability, bias detection, security, compliance, and monitoring. Be ready to explain a decision that balanced delivery speed against production stability.
Document team-building decisions
Prepare a concise example of structuring and scaling a cross-functional AI team, assigning ownership, setting expectations, and mentoring senior engineers or architects. Distinguish your direct contributions from the team's results.
Practice an executive-level briefing
Prepare a short explanation of an AI architecture decision for senior leaders or clients: business value, alternatives, cost and risk, delivery status, and the decision needed. The posting includes executive communication and support for pre-sales solutioning.
Write targeted employer questions
Ask which use cases have priority, what data and platform capabilities already exist, how success will be measured, what team the architect would build, and where technical decision authority sits. These are questions to resolve, not assumptions about BlitzenX.

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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