New opportunity

AI Engineer

Helo.ai by VivaConnect · Mumbai, India

About this role

Helo.ai by VivaConnect seeks an AI Engineer to develop and deploy conversational AI and automation solutions for customer engagement across WhatsApp and other digital channels. This full-time role is based in the Andheri, Mumbai office, works on-site Monday to Friday, and requires collaboration with product, engineering, and business teams.

Responsibilities

Design scalable chatbot workflows and assistants using NLP and generative AI; improve intent detection, response quality, personalization, and conversation performance through prompt engineering.
Integrate LLM APIs into production systems, develop prompt pipelines and response orchestration, refine datasets for contextual accuracy, and generate structured responses for enterprise workflows.
Connect AI workflows with CRM and CPaaS platforms; build API-driven automation for WhatsApp, SMS, email, and voice channels.
Translate business requirements into AI solutions, contribute to conversational architecture and cloud deployment, and improve service reliability and performance.
Monitor model performance and conversation success rates, test and iterate to improve automation accuracy, and collaborate across teams on AI solution delivery. Success is measured through scalable deployments, LLM integrations, automation efficiency, chatbot response accuracy, and collaboration.

Requirements

B.E. or B.Tech in Computer Engineering, AI, IT, or a related field, with 3–5 years of experience in AI engineering, NLP, or automation development.
Experience with NLP, conversational AI or generative AI solutions; hands-on LLM API integration; prompt engineering and response optimization; exposure to chatbot orchestration frameworks or automation pipelines; and understanding of REST APIs and JSON workflows.
Familiarity with Python, OpenAI or other LLM APIs, conversational automation platforms, and cloud platforms such as AWS, Azure, or GCP. Strong problem-solving, ownership, communication, and cross-functional collaboration are valued.

Preferred

Experience deploying AI solutions in production and certifications in AI/ML, NLP, or cloud technologies. The role follows defined KPIs and KRAs for performance tracking.

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

Skills for this role

PythonConversational AINatural Language ProcessingGenerative AILLM APIsOpenAIPrompt EngineeringChatbot DevelopmentIntent DetectionREST APIsJSONAPI IntegrationCRM IntegrationCPaaSAWSAzureGoogle Cloud PlatformAI Workflow OrchestrationAnalytical Problem-SolvingStakeholder 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

Scalable conversational workflows
The role designs NLP- and generative-AI chatbot workflows and assistants for customer engagement, with emphasis on intent detection, response quality, personalization, and conversation performance.
Production LLM integration
The role integrates LLM APIs into production-ready systems, builds prompt pipelines and response orchestration, refines datasets for contextual accuracy, and generates structured responses for enterprise workflows.
Cross-channel automation
The role connects AI workflows to CRM and CPaaS platforms and builds API-driven automation across WhatsApp, SMS, email, and voice.
Reliable solution delivery
The role translates business requirements into AI solutions, contributes to scalable conversational architecture and cloud deployment, and works with product, engineering, and business teams.
Measured improvement
The role monitors model performance and conversation success rates, then tests and iterates to improve automation accuracy; the posting also names response accuracy and automation efficiency as indicators of success.

Likely challenges to think through

Balancing response quality with scale
Preparation hypothesis: richer context and personalization may complicate fast, reliable conversations at scale. Prepare to explain how you would structure prompt pipelines and orchestration, then test intent detection and response quality against conversation outcomes.
Maintaining consistency across channels
Preparation hypothesis: CRM and CPaaS integrations may need to preserve useful context while handling different WhatsApp, SMS, email, and voice workflows. Prepare an API-driven design example showing where channel-specific behavior belongs and how structured responses are validated.
Turning business goals into measurable behavior
Preparation hypothesis: stakeholder requests may be broader than what conversation metrics can capture. Prepare to describe how you would define a workflow with product and business partners, select success measures, and use monitoring and iteration to address accuracy or reliability trade-offs.

Practice your answers

Technical practice

How would you design a scalable chatbot workflow for customer engagement?

What to coverSuggested preparation: Explain how you would map user intents to conversation paths, handle unclear requests, and keep responses consistent. Discuss how you would test the flow as usage grows.

How would you improve intent detection and response quality through prompt engineering?

What to coverSuggested preparation: Describe how you would identify misclassified intents or weak responses, revise prompts, and compare results on representative conversations. Address the trade-off between more specific instructions and flexibility across customer requests.

What would you consider when integrating an LLM API into a production application?

What to coverSuggested preparation: Cover request and response handling, error paths, latency, and how the integration fits into an existing service. Explain how you would test the integration before and after deployment.

How would you orchestrate prompts and responses across a multi-step conversation?

What to coverSuggested preparation: Explain how you would retain relevant context, choose the next step, and handle responses that do not fit the expected flow. Distinguish orchestration logic from what you ask the model to do in a prompt.

How would you refine data to improve contextual accuracy in an assistant?

What to coverSuggested preparation: Describe how you would find examples where context was missed, improve the data used by the workflow, and evaluate whether accuracy changed. Explain how you would avoid improving one set of examples at the expense of other conversations.

How would you generate structured responses for an enterprise workflow?

What to coverSuggested preparation: Discuss defining a JSON output shape, validating model responses, and handling missing or invalid fields. Explain why downstream automation needs dependable structure rather than just a plausible text response.

How would you connect a conversational AI workflow to CRM and CPaaS platforms?

What to coverSuggested preparation: Walk through the REST API and JSON exchanges needed to receive an event, use relevant customer context, and trigger an appropriate action. Address integration failures and how you would prevent an unsuccessful step from silently disrupting the workflow.

What changes when deploying automation across WhatsApp, SMS, email, and voice?

What to coverSuggested preparation: Compare how message format and conversational pacing could affect the same underlying workflow on different channels. Explain which logic you would share and which channel-specific behavior you would test separately.

How would you approach cloud deployment for a reliable conversational AI service?

What to coverSuggested preparation: Outline a scalable service design and how you would observe availability, latency, and failures after deployment. Discuss reliability and performance trade-offs without assuming a particular cloud provider or architecture.

How would you measure and troubleshoot a drop in conversation success rates?

What to coverSuggested preparation: Explain how you would examine conversation outcomes alongside intent detection, response accuracy, and automation results to locate the failure. Describe a test-and-iterate approach that checks whether a proposed fix improves performance.

Behavioural practice

Tell me about a time you translated a business need into an automation solution.

What to coverSuggested preparation: Give the situation, the requirements you clarified, and the actions you took to turn them into a workable design. Close with the result and how you checked that it addressed the original need.

Describe a time you collaborated across product, engineering, and business stakeholders.

What to coverSuggested preparation: Set out the situation and where perspectives differed, then explain how you communicated decisions and coordinated actions. State the result, including what the collaboration changed.

Tell me about a service or workflow issue you took ownership of resolving.

What to coverSuggested preparation: Describe the situation, your role in diagnosing the issue, and the actions you took with others to restore or improve the service. Give the result and what you learned about reliability.

Give an example of improving an AI or automation workflow through testing and iteration.

What to coverSuggested preparation: Explain the initial problem, the evidence you used to choose a change, and how you tested it. Report the observed result without implying that every change was successful.

Describe how you handled changing priorities in a fast-paced product environment.

What to coverSuggested preparation: Provide a specific situation, how you assessed competing work and communicated trade-offs, and what you did next. Explain the outcome for delivery or stakeholder alignment.

Sharpen your knowledge

Topics to prepare

Conversational AI and intent detection
The posting calls for NLP-based chatbot workflows, better intent detection, and response quality. Suggested revision: sketch a customer-engagement conversation with several intents, ambiguous messages, and appropriate fallback paths; explain how you would assess response quality.
Prompt pipelines and contextual accuracy
The role includes prompt engineering, response orchestration, dataset refinement, and structured responses. Suggested revision: diagram how customer context enters a prompt pipeline and where you would check the accuracy and structure of its output.
Scalable conversational architecture
The posting asks for scalable workflows, production-ready LLM integrations, and reliable AI services. Suggested revision: draw an architecture from incoming message to response, then explain how it would handle API failures and increased traffic.
Channel and platform integration
The role connects AI workflows with CRM and CPaaS platforms across WhatsApp, SMS, email, and voice. Suggested revision: trace what customer context and response data would need to pass between a channel, the AI workflow, and a CRM.
Conversation performance measurement
The posting specifies monitoring model performance and conversation success rates, then testing and iterating to improve automation accuracy. Suggested revision: define a small set of conversation outcomes and explain how you would investigate a drop in success rate.

Skills to revise

Integrate an LLM API with Python
Hands-on LLM API integration and familiarity with Python are stated. Suggested exercise: write a small Python flow that sends a customer message to an LLM API, handles an unsuccessful call, and returns a usable response.
Build and refine prompts
Prompt engineering, response optimization, and personalization are explicit responsibilities. Suggested exercise: draft two prompt versions for one customer intent, test them against ambiguous inputs, and compare accuracy and consistency.
Validate structured JSON responses
The posting names REST APIs, JSON workflows, and structured response generation. Suggested exercise: specify a JSON response for an enterprise workflow and test how your code handles missing fields or malformed output.
Prototype API-driven orchestration
The role calls for API-driven pipelines linking conversational automation with CRM and CPaaS platforms. Suggested exercise: outline or prototype a message flow with REST requests between a channel, an AI service, and a CRM; identify where failures need handling.
Test and iterate on conversation flows
Improving automation accuracy through testing and iteration is a stated responsibility. Suggested exercise: assemble a small set of representative customer messages, record intent and response outcomes, revise the workflow, and compare results.

Your preparation checklist

Map the stated requirements to evidence
Prepare a one-page list of relevant work, education and tools, including NLP or conversational AI, LLM APIs, Python, REST APIs and JSON. Note which examples are production deployments, if any; do not present preferred experience or certifications as mandatory.
Rehearse a project walkthrough
Choose one relevant project, if available, and explain the problem, your contribution, architecture, deployment decisions and results. Use actual measures of response accuracy, automation or reliability where you have them.
Sketch a conversational workflow
For a practice exercise, diagram a customer-engagement assistant with intent detection, context handling, personalization and a fallback path. Explain how the flow could scale across conversations.
Prepare an LLM integration example
Show code or a design for a Python-based LLM API call, prompt pipeline and structured JSON response. Be ready to explain response orchestration and how you would test contextual accuracy.
Trace an integration across channels
Draw a proposed REST API flow connecting the assistant to a CRM or CPaaS platform and one channel such as WhatsApp. Identify the data exchanged and how the design might extend to SMS, email or voice.
Define a testing and monitoring plan
Draft a small test set and explain how you would track conversation success, response quality, automation accuracy and service reliability. Include how test results would guide iterations; these are preparation suggestions, not stated employer procedures.
Prepare collaboration examples and questions
Rehearse how you would turn a business request into an AI solution with product and engineering partners. Bring questions about the current conversational architecture, supported channels, deployment environment and the KPIs or KRAs used for this 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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