Lead - AI Engineer

What matters most

• Deliver AI systems end to end

  The role owns solutions from requirements gathering through deployment and monitoring, including taking proofs of concept into production. Prepare a concise example of how a solution evolved across those stages and what changed before release.

• Design practical AI architectures

  The posting calls for scalable LLM applications, RAG pipelines, NLP workflows, and document-processing systems. Prepare to explain architecture choices for one relevant system, including why the approach fit its requirements.

• Lead engineering delivery

  The lead provides technical direction, mentors teams, reviews code, and upholds engineering practices. Prepare an example showing how technical guidance improved a deliverable without losing sight of the timeline.

• Own several projects at once

  The role is accountable for deliverables across 2–3 parallel projects and sets timelines based on complexity and team capacity. Prepare to explain how you would surface risks, allocate attention, and revise a plan when priorities compete.

• Shape client expectations and estimates

  Client requirements discussions, feasibility recommendations, and technical input to proposals are explicit duties. Prepare to explain how you would turn an uncertain request into assumptions, a realistic estimate, and a clear recommendation.

Likely challenges to think through

• Moving from promising prototype to reliable deployment

  A likely difficulty is balancing AI quality with API integration, deployment, and monitoring; this is a preparation hypothesis, not a reported problem at iTech. Prepare a production-transition example that identifies the risks discovered after the proof of concept and the decisions made to address them.

• Making cost-conscious architecture choices

  Because the role assesses technical risk and cost-effective approaches, a likely trade-off is capability versus operating cost and complexity across LLM, RAG, and document-processing options. Prepare to compare plausible designs against a stated use case rather than assuming the most elaborate design is best.

• Resolving competing delivery expectations

  With parallel projects, team capacity, and client discussions in scope, a likely challenge is pushing back on an unrealistic timeline while preserving a workable delivery plan. Prepare an example of how you would communicate constraints, options, and revised milestones.

Topics to prepare

• LLM, RAG and NLP system design

  The posting calls for scalable LLM applications, RAG pipelines and NLP workflows. Revise by drawing an architecture for intelligent search, then explain how you would assess retrieval quality, answer quality and scalability.

• Document AI and OCR design

  iTech identifies document AI and OCR as focus areas. Map an invoice or legal-document workflow from extraction through validation and downstream use, noting where errors could affect results.

• Proof-of-concept to production lifecycle

  The role requires experience taking AI solutions into production and leading work through monitoring. Outline the decisions and checks needed to turn a document-AI proof of concept into a deployed, monitored service.

• Cloud and on-premises trade-offs

  The role includes coordinating cloud or on-premises deployments, with exposure to AWS, GCP or Azure required. Compare those deployment approaches for an API-first AI solution, including operational and cost considerations.

• Feasibility and delivery across projects

  The lead owns deliverables across 2–3 parallel projects and must identify risks, recommend practical approaches and set realistic timelines. Practice a short feasibility assessment that states assumptions, major risks and how team capacity affects an estimate.

Skills to revise

• Build a Python RESTful API

  Strong Python and RESTful API development are required. Implement a small document-processing endpoint, with clear request validation, error responses and readable code.

• Prototype a document-search workflow

  The posting names document processing and RAG; vector-database experience is preferred. As a preparation exercise, build a small workflow that extracts document text, retrieves relevant passages and produces an answer, then inspect failure cases.

• Package and automate delivery

  Hands-on Docker and CI/CD experience is required. Containerize the sample API and sketch a CI/CD pipeline that runs checks before deployment.

• Review code and guide improvements

  The role includes code reviews, mentoring and engineering best practices. Review a short Python change and practice giving specific feedback on correctness, maintainability and production readiness.

• Scope work with a client

  Client discussions, technical recommendations and effort estimates are part of the role. Role-play a requirements conversation, identify unanswered questions, then present a bounded estimate and push back on one unrealistic assumption.

• Plan deployment and monitoring

  The posting calls for deployment and monitoring across cloud or on-premises environments; production-monitoring exposure is preferred. Draft a deployment checklist and identify signals you would inspect after releasing the sample API.

• Prepare a production AI project walkthrough

  Select a project you can discuss from requirements and proof of concept through deployment and monitoring. Prepare a diagram, your decisions, measurable results, and lessons learned; this follows the role’s end-to-end delivery responsibility.

• Practice a document-AI and RAG design exercise

  Sketch an OCR-to-search or RAG system, including document processing, retrieval, APIs, evaluation, and deployment choices. These are preparation examples based on iTech’s stated AI work, not a specified interview task.

• Gather evidence of hands-on engineering

  Prepare specific examples of Python, RESTful APIs, Docker, CI/CD, and clean-code or code-review decisions. Be ready to explain your contribution and how each example reached production.

• Show how you lead parallel delivery

  Outline a real example of directing or mentoring engineers while managing competing deliverables. Prepare the priorities, capacity assumptions, risks, timeline decisions, and outcomes; the role calls for ownership across 2–3 projects.

• Rehearse a client feasibility discussion

  Practice clarifying an AI request, identifying technical risks, proposing a practical approach, and explaining an effort estimate or respectful pushback. Client discussions and pre-sales technical input are stated responsibilities.

• Prepare trade-offs for sensitive deployments

  Be ready to discuss cloud versus on-premises deployment, production monitoring, and cost trade-offs. If you have relevant work in BFSI, healthcare, legal, or HR, prepare a concrete example; those domains and cost optimization are preferred, not required.

• Write targeted questions for iTech

  Ask how the lead divides time across projects, what deployment and monitoring responsibilities look like, and how the team evaluates solution feasibility. Clarify the experience range: the page header says 6–10 years, while Required Experience says 8–12 years.