Tech Lead- AI/ML Applications Engineer

What matters most

• Production RAG and LLM adaptation

  Design production-grade RAG pipelines and fine-tune LLMs for enterprise voice and chat bots. Prepare to explain the design choices and evaluation evidence behind a production implementation.

• End-to-end FastAPI ownership

  Architect scalable RESTful APIs and own them from design through testing and deployment. The posting emphasizes Python async fundamentals, clean code, API design, and test coverage.

• Multi-step customer interactions

  Build agentic workflows with LangChain and LangGraph for complex, real-world interactions. Prepare to explain why a multi-step workflow was needed and how its behavior was controlled.

• Reliable enterprise deployment

  Take AI models from prototype to observable, reliable services on Kubernetes-based infrastructure. Success is described in terms of shipped bot features and measurable reliability and performance improvements.

• Hands-on architecture leadership

  Work with product, data science, and DevOps on architecture and the AI roadmap. The posting expects the lead to become a go-to contributor to architecture decisions and a multiplier for the team within six months.

Likely challenges to think through

• Balancing response quality and voice latency

  Preparation hypothesis: RAG and multi-step reasoning may improve answer quality while slowing voice interactions. Prepare to discuss how you would measure latency and accuracy, choose trade-offs, and handle fallback behavior; the posting identifies these as relevant voice-bot concerns.

• Keeping agent workflows dependable

  Preparation hypothesis: More steps can handle complex requests but create more paths that need testing and monitoring. Prepare an example of how you would define workflow boundaries, verify outcomes, and respond when a step fails.

• Moving from prototype to stable service

  Preparation hypothesis: A model that works in development may behave differently under high-volume enterprise traffic. Prepare to explain how API testing, Kubernetes deployment, observability, and coordination with other teams would inform production decisions.

Topics to prepare

• Production RAG design

  The role explicitly calls for production-grade RAG pipelines. As preparation, diagram how an enterprise knowledge source becomes a grounded bot response, then identify where retrieval quality, access boundaries and response latency could fail.

• LLM fine-tuning decisions

  Production LLM fine-tuning is a stated requirement. Revise when fine-tuning is preferable to changing retrieval or instructions; sketch a baseline comparison and how you would judge whether a tuned model is ready to serve.

• Multi-step agent workflow design

  The posting calls for LangChain and/or LangGraph experience with complex customer interactions. Trace a hypothetical interaction through several steps, including a failed step, and explain how the workflow reaches a safe outcome.

• Enterprise-scale architecture

  High-volume system design and familiarity with MongoDB, PostgreSQL and MySQL are explicit. Sketch a voice or chat bot service under rising traffic; explain API, model-serving and data-storage bottlenecks and the trade-offs you would make.

• Production reliability and evaluation

  The role owns observable, reliable AI services on Kubernetes. Revise what you would measure from specification through monitoring. As a suggested extension, compare two bot versions using reliability, latency and response-quality measures; A/B evaluation is listed as an advantage, not a basic requirement.

• Voice-bot operating constraints

  Voice-bot experience is preferred. Rehearse a customer interaction with a slow or inaccurate response and explain latency, fallback and enterprise-integration trade-offs without treating prior voice-bot work as a basic requirement.

Skills to revise

• Build and test an asynchronous FastAPI endpoint

  Python async, API design, test coverage and FastAPI are explicit. Implement a small RESTful bot endpoint, add tests for valid requests and failures, and explain how asynchronous calls affect concurrent requests.

• Implement a small RAG flow

  Hands-on production RAG experience is required. On permitted sample content, build a retrieval-to-answer flow and inspect cases where the retrieved material is irrelevant or insufficient. This is a practice exercise, not a specified employer task.

• Run a fine-tuning comparison

  Fine-tuning LLMs in production is explicitly sought. If practice resources are available, run a small experiment on permitted data, compare results with an untuned baseline, and document limitations and a release decision.

• Construct a multi-step agent workflow

  Use LangChain and/or LangGraph to prototype a customer request that requires more than one step. Test a failed step and explain the workflow's next action; practical depth in these frameworks is explicitly emphasized.

• Deploy and inspect a model service on Kubernetes

  Kubernetes production deployment and monitoring are stated responsibilities. In a practice environment, deploy a sample serving endpoint, check its behavior under load, and rehearse diagnosing a failure. The sample deployment is a suggested exercise.

• Work through bot data-storage choices

  The posting asks for familiarity with MongoDB, PostgreSQL and MySQL. Draft a small conversation and retrieval-metadata model, then explain how you would store and query it using the stated databases; do not assume Kapture uses a particular schema.

• Prepare a RAG and fine-tuning walkthrough

  The role calls for production RAG and LLM fine-tuning. If you have relevant work, prepare one architecture diagram explaining data flow, evaluation, deployment decisions and measured results; otherwise, prepare a clearly labeled design exercise.

• Map a multi-step customer interaction

  The posting names LangChain and LangGraph. Sketch an agentic workflow for a complex customer request, including tool use, state transitions, failure handling and how you would test it.

• Rehearse a FastAPI system design

  Design a scalable REST API for an AI-powered chat feature. Be ready to explain async Python choices, data storage trade-offs involving MongoDB, PostgreSQL or MySQL, test coverage and deployment.

• Trace the path from prototype to production

  The role includes Kubernetes-based deployment and observable, reliable services. Prepare a release and monitoring plan covering performance measures, failure response and ownership after launch.

• Work through voice-bot trade-offs

  Voice bots are part of the role, and voice or conversational AI experience is preferred. Prepare an example or hypothetical design that addresses latency, accuracy, fallback handling and enterprise integration.

• Show technical leadership with evidence

  Prepare a concise account of an architecture decision and how you worked with product, data science or DevOps. For past work, use actual interaction volumes, customer scale and reliability results where available rather than estimates presented as facts.

• Bring targeted questions for Kapture CX

  Ask how the core AI platform team evaluates RAG and bot quality, what production reliability challenges matter most, and how architecture decisions are shared across teams. Confirm the Bangalore five-day office arrangement if it affects your plans.