New opportunity

Artificial Intelligence Engineer

People impact · Bangalore, India

About this role

People impact seeks an Artificial Intelligence Engineer to lead enterprise adoption of autonomous and agent-based AI. The role combines AI strategy, enterprise architecture and digital transformation to develop scalable systems for autonomous decision-making. The posting specifies 6–9 years of experience and an on-site, permanent position in Bangalore, Karnataka, India, with compensation of ₹18–22 lacs per annum.

Responsibilities

Develop an enterprise Agentic AI strategy and long-term roadmap aligned with business priorities, and lead large-scale transformation initiatives.
Design and implement scalable AI agents and autonomous decision-making frameworks. Establish governance, compliance and responsible AI practices for enterprise deployment.
Coordinate complex AI programs across functions and work with business and technology stakeholders to integrate solutions into operational workflows.
Manage relationships with technology vendors and partners, foster innovation and contribute thought leadership on Agentic AI.

Required qualifications

Proven expertise in Agentic AI, autonomous systems and LLM-powered agent frameworks, with hands-on AI architecture, solution design and scalable system development experience.
Experience designing and deploying enterprise-scale AI platforms; knowledge of AI governance, compliance standards and risk management frameworks.
Technical leadership and program management ability, including translating business challenges into practical AI solutions.

Preferred qualifications and additional competencies:

Experience with multi-agent systems and orchestration frameworks; exposure to Generative AI, LLMs and decision intelligence platforms.
Understanding of enterprise AI adoption and operational scalability, and familiarity with AWS, Azure or Google Cloud Platform. At least 3 years in enterprise Agentic AI architecture is preferred.
Strategic planning, team leadership, vendor management and executive stakeholder collaboration, including C-suite engagement. Exposure to M&A technical due diligence is an added advantage. The posting also lists high- and low-level design, prompt engineering and RAG among its skills.

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

Skills for this role

Agentic AIAutonomous systemsAI agentsMulti-agent systemsLLMsGenerative AIRAGPrompt EngineeringAI architectureHigh Level Design (HLD)Low Level Design (LLD)OrchestrationAI governanceAI complianceRisk managementResponsible AIProgram managementStrategic planningStakeholder managementVendor managementDecision intelligenceAWSAzureGoogle Cloud Platform (GCP)

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 Agentic AI roadmap
The role develops and executes a long-term strategy aligned with business priorities and leads transformation initiatives. Prepare to explain how you would prioritize use cases and turn a roadmap into deliverable programs.
Scalable agent architecture
The posting calls for hands-on design and implementation of AI agents, autonomous decision-making frameworks, and enterprise-scale platforms. Prepare to discuss architecture decisions, orchestration, and the path from design to deployment.
Governance for autonomous systems
Establishing governance, compliance, responsible AI, and risk practices is an explicit duty. Prepare to explain how oversight and risk controls would be incorporated into an enterprise deployment.
Operational adoption and program execution
The role coordinates complex cross-functional programs and works with business and technology stakeholders to embed AI in operational workflows. Prepare an approach for translating a business problem into a practical solution and coordinating its rollout.
Senior stakeholder and partner leadership
The posting includes executive collaboration, technical leadership, and managing vendor and partner relationships. Prepare to explain how you would communicate architectural choices and work with external technology partners.

Likely challenges to think through

Balancing autonomy with oversight
A likely challenge is preserving useful autonomous decision-making while meeting the posting’s governance and risk expectations; this is a preparation hypothesis, not a stated employer problem. Prepare to discuss where human review, monitoring, or limits on agent actions would fit.
Moving from roadmap to scalable deployment
The role spans strategy, hands-on architecture, and enterprise deployment, so a plausible difficulty is choosing what to implement first without compromising long-term scalability. Prepare to explain sequencing and the trade-offs behind an initial architecture.
Embedding AI across functions
Because the role must coordinate stakeholders and integrate solutions into workflows, a plausible challenge is reconciling different operational needs with a coherent enterprise approach. Prepare to describe how you would establish shared priorities and resolve design trade-offs.

Practice your answers

Technical practice

How would you turn business priorities into an enterprise Agentic AI roadmap?

What to coverSuggested answer focus: Describe how you would identify suitable use cases, define outcomes, and sequence pilots and wider deployment. Explain trade-offs among value, technical feasibility, risk, and operational readiness.

How would you design an enterprise-scale LLM-powered agent system?

What to coverSuggested answer focus: Walk from high-level architecture to component-level design, including agent responsibilities, integrations, and deployment boundaries. Explain how the design would support reliability, maintainability, and growth.

When would you use multiple agents rather than one agent?

What to coverSuggested answer focus: Compare the benefits of specialized agents with orchestration complexity, latency, and failure handling. Describe how you would assign responsibilities and coordinate their work.

How would you decide whether prompt engineering or RAG is needed for an agent?

What to coverSuggested answer focus: Explain what prompting can achieve on its own and when retrieval could supply necessary context. Discuss how you would assess answer quality, source relevance, and failure cases.

How would you set boundaries for autonomous decision-making?

What to coverSuggested answer focus: Describe which decisions an agent could take independently and which would need review or escalation. Address traceability, risk, and how the boundaries would reflect business requirements.

How would you diagnose an agent platform that works in a pilot but struggles at enterprise scale?

What to coverSuggested answer focus: Describe how you would isolate bottlenecks across orchestration, integrations, model use, and operational workflows. Explain the evidence you would gather before changing the architecture.

What governance would you establish before deploying agents across an enterprise?

What to coverSuggested answer focus: Cover ownership, access, oversight, monitoring, and a process for reviewing risks and compliance obligations. Explain how governance would remain practical as deployments expand.

How would you evaluate whether an AI agent is ready for an operational workflow?

What to coverSuggested answer focus: Define task-level success measures and test representative errors, edge cases, and escalation paths. Explain how you would weigh performance against risk and the workflow's tolerance for mistakes.

How would you assess a cloud environment for an enterprise agent platform?

What to coverSuggested answer focus: Compare relevant architecture considerations across AWS, Azure, or Google Cloud Platform without assuming a required provider. Address integration, scalability, governance needs, and operational trade-offs.

How would you translate a business challenge into an AI solution embedded in an existing workflow?

What to coverSuggested answer focus: Start with the decision or task to improve, then identify the people, systems, and information the solution must work with. Explain how you would decide whether an agent is appropriate and define a practical path to adoption.

Behavioural practice

Tell me about a time you aligned business and technology stakeholders on an AI initiative.

What to coverSuggested answer focus: Use a real situation, the differing priorities, and the actions you took to reach a workable decision. State the result and how it affected delivery or adoption.

Describe a complex transformation program you led through a setback.

What to coverSuggested answer focus: Explain the situation, your leadership actions when plans changed, and how you coordinated the people involved. Give the result, including what remained unresolved if applicable.

Tell me about a time you presented a technical recommendation to executive stakeholders.

What to coverSuggested answer focus: Set out the decision they faced, how you communicated options and risks, and your own actions. Describe the outcome without claiming agreement or impact that you cannot substantiate.

Describe a time you managed a technology vendor or partner relationship.

What to coverSuggested answer focus: Give a real situation, the expectations or tensions involved, and what you did to manage the relationship. Explain the result and any trade-offs in the decision.

Tell me about a time you led a team toward a new AI approach.

What to coverSuggested answer focus: Describe the opportunity, how you evaluated it with the team, and the actions you took to move from idea to decision. State the result and what the experience taught you about responsible adoption.

Sharpen your knowledge

Topics to prepare

Agentic AI and autonomous decision-making
The posting requires expertise in Agentic AI, autonomous systems, and LLM-powered agent frameworks. Suggested revision exercise: explain how an agent chooses actions, uses information, and handles a decision it should not make autonomously.
Enterprise-scale AI architecture
The role requires architecture and solution design for scalable AI platforms. Suggested revision exercise: sketch an architecture from an operational workflow through an AI agent to its decision and oversight points; explain how the design could scale.
AI governance, compliance, and risk
The posting requires governance, compliance, responsible AI, and risk-management knowledge. Suggested revision exercise: identify risks in an autonomous decision workflow and describe proportionate controls and review points without assuming a particular regulatory standard.
Enterprise strategy and adoption
The role calls for an Agentic AI roadmap aligned with business priorities and large-scale transformation. Suggested revision exercise: outline how to prioritize two hypothetical business use cases and phase their adoption into operational workflows.
Multi-agent orchestration
Multi-agent systems and orchestration frameworks are preferred, rather than required. Suggested revision exercise: compare a single-agent design with a coordinated multi-agent design, including when added coordination would not be worthwhile.

Skills to revise

Produce high- and low-level designs
Hands-on architecture and solution design are required; HLD and LLD appear in the posting's skills list. Suggested exercise: draw both design levels for a scalable agent workflow, showing components, interfaces, and where decisions are controlled.
Build an LLM-powered agent workflow
The posting requires hands-on scalable system development and lists prompt engineering and RAG as skills. Suggested exercise: implement a small agent workflow using a prompt and retrieved context, then test what it does when context is missing or conflicting.
Plan an enterprise deployment
Experience designing and deploying enterprise-scale AI platforms is required. Suggested exercise: write a deployment checklist covering integration into an existing workflow, operational scaling, governance checks, and failure handling.
Translate a business problem into an AI solution
The posting requires translating business challenges into practical AI solutions and coordinating cross-functional programs. Suggested exercise: turn a hypothetical business request into a scoped use case, delivery milestones, stakeholder responsibilities, and a criterion for whether agent-based AI is appropriate.
Present a technology and vendor decision
The role includes vendor relationships and executive stakeholder management; cloud ecosystem familiarity is preferred. Suggested exercise: compare two hypothetical solution or vendor options against architecture, risk, and operational needs, then summarize the trade-offs in a short executive briefing.

Your preparation checklist

Draft an enterprise AI roadmap
The role calls for an Agentic AI strategy aligned with business priorities. Prepare a one-page, hypothetical roadmap showing use-case selection, sequencing and measures of operational value.
Prepare a platform walkthrough
The posting requires hands-on architecture and enterprise-scale deployment experience. If available, outline one relevant project’s business problem, design decisions, deployment approach, scaling challenges and your specific contribution.
Practice an agent-system design exercise
As interview preparation, sketch a hypothetical autonomous workflow with a high-level diagram and key low-level decisions. Explain where LLM-powered agents, orchestration or RAG would help—and where they would not.
Document governance decisions
The role explicitly includes governance, compliance, responsible AI and risk management. Prepare a short decision record covering oversight, access controls, monitoring and how an unsafe agent action would be handled.
Map a transformation program
The posting calls for cross-functional execution and integration into operational workflows. Prepare an example plan identifying business and technology stakeholders, dependencies, decision points and rollout measures.
Gather leadership and partner evidence
Technical leadership, executive collaboration and vendor management appear in the posting. Prepare concise examples, where available, of decisions you led, trade-offs you communicated and technology-partner evaluations.
Prepare questions for People impact
Ask which enterprise workflows are the initial priority, what agent systems or platforms already exist, how governance decisions are owned, and what outcomes this role is expected to deliver first.

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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