New opportunity

Principal AI Engineer

Cornerstone · Hyderabad, India

About this role

The Principal AI Engineer will design, build, and scale AI-driven systems across Cornerstone’s product platforms. This office-based role in Hyderabad, India combines hands-on engineering with leadership of multi-team technical initiatives, working with product, architecture, engineering, data, UX, security, and DevOps teams.

Responsibilities

Build and operate AI-powered applications, including LLM-based services, ML-driven workflows, and intelligent automation. Integrate AI into Java backend systems and expose capabilities through scalable REST and GraphQL APIs.
Develop data pipelines for feature engineering, retrieval-augmented generation, vector search, and inference. Apply prompt design, model orchestration, evaluation, and observability practices.
Build cloud-native systems on AWS, including EC2, S3, RDS, Lambda, and EKS. Use Docker, Kubernetes, and infrastructure as code with Terraform or AWS CDK; optimize scalability, latency, reliability, and cost.
Design SQL and NoSQL data stores for distributed systems, make trade-offs among consistency, availability, and performance, and use event-driven or asynchronous architectures where appropriate.
Lead initiatives from design to production, influence architecture and engineering standards through reviews and guidance, and mentor senior engineers.

Required qualifications

8–10 years of hands-on software engineering experience building large-scale production systems, including 3+ years building AI-powered applications such as ML or LLM-based systems.
Strong hands-on Java backend and distributed-systems experience; mandatory experience designing and operating cloud-native workloads on AWS.
Production experience with SQL and NoSQL databases, REST and GraphQL API-first architectures, Docker, and Kubernetes (EKS). Strong problem-solving skills and ability to own systems end to end.

Preferred qualifications

Experience with AWS Bedrock, OpenAI APIs, LangChain, or LlamaIndex; vector databases and semantic search; MLOps, model monitoring, and evaluation frameworks; and product-driven agile environments.

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

Skills for this role

JavaAWSEC2S3RDSLambdaEKSRESTGraphQLSQLNoSQLDockerKubernetesTerraformAWS CDKLarge Language ModelsMachine LearningGenerative AIRetrieval-Augmented GenerationVector SearchFeature EngineeringPrompt DesignModel OrchestrationModel EvaluationObservabilityDistributed SystemsEvent-Driven ArchitectureInfrastructure as CodeAWS BedrockOpenAI APIs <https://openai.com/api/>

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

Production AI engineering
Design, build, and operate LLM services, ML workflows, and intelligent automation. The role also calls for pipelines supporting feature engineering, RAG, vector search, and inference, with model evaluation and observability.
Java integration and product APIs
Integrate AI capabilities into Java backend systems and expose them across products through scalable REST and GraphQL services.
AWS platform ownership
Design and operate cloud-native AI workloads on AWS using containers, EKS, and infrastructure as code; optimize scalability, latency, reliability, and cost.
Distributed data design
Design production SQL and NoSQL stores, make consistency, availability, and performance trade-offs, and use event-driven or asynchronous architecture where appropriate.
Principal-level technical leadership
Lead multi-team initiatives from design through deployment while contributing hands-on, influencing architecture and engineering standards, mentoring senior engineers, and working with product, data, UX, security, and DevOps partners.

Likely challenges to think through

AI quality versus dependable service behavior
Likely preparation hypothesis: variable model outputs may be difficult to reconcile with reliable product APIs. Prepare a concrete design walkthrough covering evaluation, observability, and how the service handles poor or unavailable model responses.
Inference performance versus cloud cost
Likely preparation hypothesis: scaling AI workloads on AWS could force choices between response latency, reliability, and spend. Prepare to explain how you would measure those trade-offs and choose deployment and scaling approaches.
Fresh data versus distributed-system resilience
Likely preparation hypothesis: retrieval or inference pipelines spanning SQL, NoSQL, and asynchronous components may face consistency and availability trade-offs. Prepare to justify data-flow boundaries, update behavior, and failure handling.
Cross-team direction versus hands-on delivery
Likely preparation hypothesis: a principal engineer may need to align several teams without losing ownership of technical details. Prepare an example of how to use design reviews and clear decisions to move an initiative from architecture to production.

Practice your answers

Technical practice

How would you integrate an AI capability into a production Java backend?

What to coverThe posting calls for AI capabilities in Java-based backend systems. Suggested preparation: explain service boundaries, failure handling, and how you would keep the integration reliable as usage grows.

How would you expose an AI capability through REST and GraphQL APIs?

What to coverThe role specifies API-first services using both REST and GraphQL. Suggested preparation: compare when each interface fits, and address request validation, response design, and the latency of AI-backed requests.

How would you design a retrieval-augmented generation pipeline with vector search?

What to coverThe posting names data pipelines for RAG and vector search. Suggested preparation: walk through document preparation, retrieval, generation, and how you would assess whether retrieved material improves answers.

How would you manage prompts and model orchestration in an LLM service?

What to coverPrompt design and model orchestration are stated responsibilities. Suggested preparation: describe how you would control changes, handle model failures, and balance answer quality against latency and cost.

How would you evaluate and troubleshoot a production AI service?

What to coverThe posting calls for evaluation and observability, with model monitoring listed as a nice-to-have. Suggested preparation: distinguish model-quality signals from service-health signals and explain how you would investigate a decline in either.

How would you choose AWS components for a scalable AI workload?

What to coverThe role names EC2, S3, RDS, Lambda, and EKS and emphasizes scalability, latency, reliability, and cost. Suggested preparation: compare suitable uses of those services for a hypothetical workload rather than assuming a specific Cornerstone architecture.

How would you deploy and operate a containerized AI service on EKS?

What to coverDocker, Kubernetes on EKS, and infrastructure as code with Terraform or AWS CDK are in the posting. Suggested preparation: cover deployment, scaling, recovery, and how you would keep infrastructure changes reproducible.

How would you select SQL and NoSQL stores for a distributed AI application?

What to coverThe role requires production SQL and NoSQL experience and trade-offs among consistency, availability, and performance. Suggested preparation: explain how access patterns and consistency needs would drive the choice.

When would you use an event-driven or asynchronous workflow rather than a synchronous request?

What to coverThe posting explicitly mentions event-driven and asynchronous architectures where appropriate. Suggested preparation: discuss latency, failure recovery, and how callers would learn the outcome of longer-running work.

How would you build a pipeline for feature engineering and inference?

What to coverThe role includes data pipelines supporting feature engineering and inference, alongside ML-driven workflows. Suggested preparation: explain how you would manage input quality, pipeline changes, and reliable delivery of inference results.

Behavioural practice

Tell me about a complex technical initiative you led from design into production.

What to coverThe Principal role owns multi-team initiatives end to end. Suggested preparation: use a real situation, explain your hands-on decisions and coordination, and state the outcome.

Describe a time you influenced an architecture decision or engineering standard across teams.

What to coverThe posting calls for influence through design reviews and technical guidance. Suggested preparation: describe the disagreement or decision, your actions, and the resulting change without overstating your authority.

Tell me about a time you mentored a senior engineer.

What to coverMentoring senior engineers is a stated responsibility. Suggested preparation: give the situation, the specific guidance you provided, and the observable result.

Describe a time you worked across product, data, UX, security, or DevOps to deliver a capability.

What to coverThe role explicitly involves collaboration with those functions. Suggested preparation: choose a real example, explain how you resolved differing priorities, and describe the delivery outcome.

Tell me about a production problem you owned through resolution.

What to coverThe posting emphasizes operating reliable systems, problem-solving, and end-to-end ownership. Suggested preparation: explain the situation, your diagnosis and actions, and what changed afterward.

Sharpen your knowledge

Topics to prepare

Java backend and API architecture
The posting requires hands-on Java, distributed systems, and REST and GraphQL API-first design. Sketch an AI capability exposed through both API styles; explain service boundaries, failure handling, and how the interfaces would scale.
Production AI system design
The role covers LLM services, ML workflows, feature engineering, RAG, vector search, and inference. Trace a request from data preparation through retrieval or inference to response, identifying where evaluation and observability belong.
AWS cloud architecture
Hands-on design and operation of AWS workloads is mandatory; the posting names EC2, S3, RDS, Lambda, and EKS. Compare two plausible deployments for an AI service and justify latency, reliability, scalability, and cost trade-offs.
Distributed data trade-offs
Production SQL and NoSQL experience is required. For an AI-enabled workflow, choose where each store fits and explain consistency, availability, and performance trade-offs, including when asynchronous processing is appropriate.
Container and infrastructure design
The posting requires Docker and Kubernetes (EKS) and calls for infrastructure as code using Terraform or AWS CDK. Review how a containerized service would be deployed, updated, and recovered from failure on EKS.
Principal-level technical leadership
The role owns multi-team initiatives and influences standards through reviews and guidance. Rehearse a design-review scenario in which product, data, security, and DevOps stakeholders disagree; explain how you would resolve trade-offs and guide the work to production.

Skills to revise

Implement a Java AI service interface
Suggested exercise: build a small Java service with REST and GraphQL endpoints for the same mock AI operation, including input handling and error responses. This practices the posting's required Java backend and API-first work without assuming a particular framework.
Prototype a retrieval and inference pipeline
Suggested exercise: ingest a small document set, prepare searchable representations, retrieve relevant passages, and pass them to an inference step; inspect incorrect retrievals. This maps to the stated RAG, vector-search, and pipeline responsibilities; vector-database experience is listed as nice to have.
Evaluate prompts and service behavior
Suggested exercise: create a small set of representative inputs, compare two prompt designs, record output quality, latency, and failures, and specify what production signals to monitor. The posting explicitly calls for prompt design, evaluation, and observability.
Package and deploy a cloud workload
Suggested exercise: containerize a Java service with Docker and draft an EKS deployment plus infrastructure definitions in either Terraform or AWS CDK. Explain how you would verify rollout and diagnose an unhealthy service; these technologies appear in the posting.
Review an end-to-end design
Suggested exercise: write a brief design proposal for an AI capability, then critique its API, data-store, AWS, and operational choices as if reviewing another team's proposal. This practices the role's hands-on design reviews and cross-team technical guidance.

Your preparation checklist

Map evidence to the requirements
Prepare a one-page record of actual production work covering Java, AWS, SQL and NoSQL, REST and GraphQL, Docker and EKS, plus the stated 8–10 years of software engineering and 3+ years building AI applications. Leave any unsupported areas blank rather than inventing examples.
Rehearse a production AI project walkthrough
Choose an actual ML- or LLM-based system and prepare a diagram, your hands-on contributions, design decisions, production issues and measured outcomes. The role emphasizes building AI systems and owning them through operation.
Practice a Java AI-service design
For a hypothetical product capability, sketch Java backend integration and REST and GraphQL interfaces. Show where feature engineering, retrieval or inference would fit, and how you would evaluate and observe the AI behavior; these are design topics in the posting, not claims about an existing Cornerstone system.
Draw an AWS deployment and operations plan
Prepare a hypothetical architecture using the relevant services among EC2, S3, RDS, Lambda and EKS. Explain Docker deployment, a Terraform or AWS CDK approach, and how you would measure and manage latency, reliability, scale and cost.
Explain distributed-data trade-offs
Work through a sample choice between SQL and NoSQL, including consistency, availability and performance. Add an event-driven or asynchronous path where it helps, and identify its operational trade-offs.
Document principal-level technical leadership
Prepare a real example of an initiative taken from design to production across teams. Capture your design-review contributions, technical guidance, mentoring of senior engineers and collaboration with relevant partners such as product, data, UX, security and DevOps.
Write focused questions for Cornerstone
Ask which AI product capabilities this role would tackle first, how production AI quality is evaluated, and where the Principal AI Engineer owns architecture decisions across teams. Confirm practical expectations for the advertised office-based Hyderabad role.

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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