New opportunity

Lead AI Developer

Grazitti Interactive · Panchkula, India

About this role

Grazitti Interactive is seeking a Lead AI Developer in Panchkula, India, with 5–6 years of experience to lead AI initiatives and build scalable, production-ready intelligent systems. The role combines AI architecture and development with team leadership and cross-functional collaboration.

Responsibilities

Design and deploy Python-based AI applications focused on LLM orchestration and advanced workflows. Define architecture and establish practices for designing, deploying, monitoring, and improving AI solutions.
Lead and mentor developers, conduct code reviews, provide technical guidance, and promote learning and code quality.
Manage the AI development lifecycle, including data ingestion, vectorization, deployment, and monitoring. Improve system performance and scalability.
Evaluate third-party AI tools and open-source models, apply relevant AI advances, and work with stakeholders to align solutions with business objectives.

Required skills:

Strong Python proficiency, including FastAPI, Pydantic, and AsyncIO; hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or CrewAI.
Deep understanding of prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation; experience with vector databases such as Pinecone, Weaviate, or Milvus.
Strong knowledge of AWS, Azure, or GCP; familiarity with Docker and Kubernetes; and working knowledge of PyTorch or TensorFlow.
Strong problem-solving, leadership, and communication skills.

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

Skills for this role

PythonFastAPIPydanticAsyncIOLangChainLlamaIndexCrewAIPrompt engineeringRetrieval-Augmented Generation (RAG)Model evaluationPineconeWeaviateMilvusAWSAzureGCPDockerKubernetesPyTorchTensorFlowLLM orchestrationData ingestionVectorizationAI architecturePerformance optimizationCode reviewLeadershipCommunicationProblem-solving

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 architecture
The role calls for defining AI architecture and designing and deploying scalable Python applications centered on LLM orchestration and advanced workflows.
End-to-end AI lifecycle
Ownership spans data ingestion, vectorization, deployment, and monitoring, with an emphasis on improving performance and scalability over time.
Developer leadership and code quality
The lead is expected to mentor developers, conduct regular code reviews, provide technical guidance, and promote continuous learning.
Practical technology choices and business alignment
The posting calls for evaluating third-party AI tools and open-source models, applying relevant advances, and working with stakeholders to align solutions with business objectives.

Likely challenges to think through

Balancing answer quality with production performance
Likely preparation hypothesis: RAG and LLM orchestration may create trade-offs among retrieval quality, response time, and scalability. Prepare to explain how you would evaluate model outputs, monitor a deployed workflow, and decide what to optimize.
Choosing tools without overcomplicating the architecture
Likely preparation hypothesis: comparing LLM frameworks, vector databases, and models could require weighing capability against operational complexity. Prepare a structured comparison tied to the application’s needs rather than a preferred tool.
Leading while still delivering
Likely preparation hypothesis: defining architecture while mentoring and reviewing developers’ work could compete for attention. Prepare an example of how you would set design standards, delegate implementation, and use reviews to maintain quality.

Practice your answers

Technical practice

How would you structure a production-ready Python application that orchestrates LLM workflows?

What to coverPreparation suggestion: Explain the boundaries between API, orchestration, model, and data components, and how you would handle failures across workflow steps. Discuss how those choices support maintainability and scalability.

How would you use FastAPI, Pydantic, and AsyncIO in an AI application?

What to coverPreparation suggestion: Cover request validation, API contracts, and where asynchronous execution helps with I/O-bound work. Explain how you would avoid blocking calls or uncontrolled concurrency.

How would you manage data ingestion and vectorization for a RAG system?

What to coverPreparation suggestion: Walk through preparing source data, creating embeddings, and updating indexed content. Discuss how you would detect stale or incomplete data.

How would you design and troubleshoot retrieval with a vector database?

What to coverPreparation suggestion: Explain how you would assess whether retrieved context is relevant before blaming the LLM for a poor answer. Discuss retrieval-quality and latency trade-offs when using a vector database such as Pinecone, Weaviate, or Milvus.

How would you improve a prompt without making the workflow brittle?

What to coverPreparation suggestion: Describe how you would define the desired output, test prompts against varied inputs, and handle missing or conflicting context. Distinguish a prompt problem from a retrieval or model problem.

How would you evaluate an LLM application before and after deployment?

What to coverPreparation suggestion: Describe representative test cases and measures tied to the application's intended task. Explain how you would compare changes over time and investigate regressions.

How would you deploy an AI application using cloud and container tools?

What to coverPreparation suggestion: Outline the roles of a cloud platform, Docker, and Kubernetes in packaging and operating the application. Discuss deployment reliability and scaling trade-offs without assuming a particular platform is used.

What would you monitor when a deployed AI workflow becomes slow or unreliable?

What to coverPreparation suggestion: Trace the workflow through API handling, retrieval, and model calls to locate the bottleneck or failure. Explain how observations would guide a targeted fix and confirm its effect.

How would you improve performance as usage of an AI application grows?

What to coverPreparation suggestion: Identify where concurrency, retrieval, or model calls may constrain throughput. Explain how you would weigh faster responses against resource use and answer quality.

How would you compare a third-party AI tool with an open-source model or framework?

What to coverPreparation suggestion: Define evaluation criteria from the business objective, then compare practical performance, integration effort, and operational demands. Explain what evidence would justify adopting an option rather than following a recent AI trend.

Behavioural practice

Tell me about a time you mentored a developer through a difficult technical problem.

What to coverPreparation suggestion: Use a real situation, the guidance you gave, and the result. Show how you helped the developer learn while keeping the work moving.

Tell me about a code review where you raised a significant quality concern.

What to coverPreparation suggestion: Describe the context, how you communicated the concern, and what changed as a result. Explain how you balanced code quality with a constructive working relationship.

Tell me about a time you aligned a technical solution with a stakeholder's business objective.

What to coverPreparation suggestion: State the objective, the trade-off you discussed, and the decision or outcome. Make clear how stakeholder input affected your technical approach.

Tell me about a time you resolved a problem across teams while delivering a technical project.

What to coverPreparation suggestion: Describe the situation, each team's relevant concern, your actions, and the result. Focus on how communication helped resolve a concrete delivery issue.

Tell me about a time you evaluated a new AI approach before recommending its use.

What to coverPreparation suggestion: Use a real example to explain what prompted the evaluation, how you tested its practical value, and what resulted. Include how you shared the learning with others, whether or not you adopted the approach.

Sharpen your knowledge

Topics to prepare

RAG and vector retrieval
The posting calls for deep RAG understanding and experience with vector databases. Sketch how documents move from ingestion and vectorization to retrieval and answer generation; explain how retrieval choices affect answer quality.
Prompt and model evaluation
The posting names prompt engineering and model evaluation. Compare two prompt approaches for one task and define a small evaluation set, success criteria, and failure categories.
LLM architecture and tool selection
The role defines AI architecture, designs advanced workflows, and evaluates third-party tools and open-source models. Diagram a workflow and explain the trade-offs you would use to choose a framework or model; this is a preparation exercise, not a specified project.
Production lifecycle and scalability
The role covers deployment, monitoring, performance optimization, and scalability. Walk through an AI application's lifecycle and identify where you would detect and address a latency or reliability problem.
Technical leadership and business alignment
The posting requires mentoring, code reviews, technical guidance, and stakeholder collaboration. Prepare a concise explanation of how you would resolve a code-quality disagreement while keeping an AI solution aligned with its business objective.
Machine-learning framework fundamentals
Working knowledge of PyTorch or TensorFlow is listed. Trace a small model's inference steps in one of them and explain what you would inspect if its outputs or runtime changed.

Skills to revise

Build an asynchronous Python API
The posting specifies Python, FastAPI, Pydantic, and AsyncIO. As practice, implement a small FastAPI endpoint with Pydantic request validation and an asynchronous operation; review its error handling.
Implement an LLM workflow
Hands-on experience with LangChain, LlamaIndex, or CrewAI is requested. Build a short workflow in one of those frameworks and explain each step and its failure points.
Ingest and retrieve from a vector database
The role includes ingestion and vectorization and names Pinecone, Weaviate, or Milvus. Practice loading a small document set into one of them, retrieving relevant passages, and inspecting an irrelevant result.
Run a repeatable prompt evaluation
Prompt engineering and model evaluation are explicit requirements. Create a small set of test questions, run two prompt versions against it, record outcomes, and explain which version you would retain and why.
Plan a cloud deployment
The posting names AWS, Azure, or GCP, plus familiarity with Docker and Kubernetes. Draft a deployment plan for a containerized Python AI service on one named cloud, including how you would monitor it; the plan is suggested practice, not a prescribed employer setup.
Conduct a mentoring-focused code review
The role calls for regular reviews, code quality, and developer guidance. Review a sample Python AI-service change, write actionable comments on correctness and maintainability, and practice explaining the priorities to its author.

Your preparation checklist

Select a relevant project walkthrough
Prepare a concise account of an AI application you worked on, if available: your decisions, Python implementation, LLM workflow, production constraints, and measurable outcomes. Distinguish your work from the team’s.
Sketch a scalable RAG design
As a practice exercise, diagram data ingestion, vectorization, retrieval, LLM orchestration, and an API using FastAPI, Pydantic, and AsyncIO. Explain trade-offs and where a vector database or LLM framework would fit.
Prepare an evaluation plan
Draft a small comparison of prompts, retrieval approaches, and model or third-party tool options. State how you would assess answer quality, performance, and suitability for a business objective; the posting does not prescribe particular metrics.
Explain the path to production
Prepare a deployment and monitoring outline using one of AWS, Azure, or GCP, with Docker and Kubernetes where relevant. Cover how you would identify and address performance or scalability problems.
Gather leadership examples
Choose specific examples, if you have them, of code reviews, technical guidance, mentoring, or cross-functional decisions. Be ready to explain the situation, your actions, and the result without overstating your role.
Write questions for the employer
Ask which AI initiatives need leadership first, how teams evaluate and monitor deployed systems, and how developers and stakeholders share architecture decisions. These are questions to clarify, not stated company processes.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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