GenAI Developer

What matters most

• Build LLM-powered applications

  The role designs, builds and deploys applications such as chatbots, copilots, content generation and intelligent automation, using LLM services including OpenAI, Azure OpenAI and Anthropic.

• Improve prompts

  Prompt engineering and optimization are explicit responsibilities. Prepare to explain how a prompt change would be tested against a defined application task.

• Deliver RAG and semantic search

  The role implements RAG pipelines and integrates vector databases such as FAISS, Pinecone and Weaviate. Embeddings and vector-database experience are required.

• Evaluate models and fine-tune when warranted

  LLM evaluation is a stated duty; fine-tuning is specified where required. Fine-tuning open-source models is listed as good to have, not required.

• Turn solutions into dependable services

  The role develops APIs and microservices and works with AI/ML, data and product teams to ensure scalability, security and performance. Python and REST/backend knowledge are required.

Likely challenges to think through

• Retrieval quality versus response speed

  Preparation hypothesis, not a reported Eazygurus issue: a RAG application may need more thorough retrieval without making responses too slow. Prepare to explain how you would assess retrieved results alongside answer quality and latency.

• Choosing the least complex improvement

  Preparation hypothesis: prompt changes, retrieval changes and fine-tuning can address different failures, but add different effort and operational complexity. Prepare a decision framework based on observed errors and evaluation results; the posting calls for fine-tuning only where required.

• Balancing reliability with safeguards

  Preparation hypothesis: deploying an LLM through an API may require trade-offs among throughput, performance and protection of sensitive data. Prepare to describe how you would test those trade-offs with the AI/ML, data and product teams, without assuming a particular infrastructure or company policy.

Topics to prepare

• NLP and transformer fundamentals

  Required: understanding of NLP concepts and transformer models. Exercise: explain how tokens, context and transformer attention affect an LLM’s response to a long document.

• RAG and semantic search design

  The role calls for RAG pipelines, embeddings and vector databases. Exercise: diagram a question-answering flow from document ingestion through retrieval to generation, and explain where irrelevant results could enter.

• Prompt engineering and optimization

  Building prompt strategies is a stated responsibility. Exercise: compare two prompts for a chatbot task and explain how you would judge whether the revision improved the response.

• LLM evaluation and fine-tuning decisions

  The posting calls for evaluation and fine-tuning where required; open-source model fine-tuning is good to have. Exercise: outline an evaluation set for a content-generation task and explain when prompt changes or RAG might be preferable to fine-tuning.

• GenAI service design

  The role includes APIs and microservices, with attention to scalability, security and performance. Exercise: sketch an LLM-backed API and identify how you would handle slow responses, request failures and sensitive input.

Skills to revise

• Python LLM API integration

  Strong Python and experience with OpenAI, Azure OpenAI or similar APIs are required. Practice writing a small Python function that submits a prompt, handles an API failure and returns a structured result.

• LLM framework implementation

  Hands-on experience with frameworks such as LangChain, LlamaIndex or Haystack is required. Practice building a short retrieval-to-answer workflow in one named framework and be ready to explain what each component does.

• Embedding and vector-index workflow

  The posting requires experience with embeddings and vector databases and names FAISS, Pinecone and Weaviate. Practice embedding a small document set, indexing it in one named option and inspecting the results of several semantic queries.

• Prompt testing

  Prompt optimization is a stated responsibility. Practice running a fixed set of chatbot questions against two prompt versions, recording response quality and failure cases, and explaining the change you would keep.

• REST endpoint for a GenAI feature

  REST and backend knowledge are required, and developing GenAI APIs is part of the role. Practice exposing a simple Python endpoint for an LLM-backed feature, validating its input and returning a clear error when the LLM call fails.

• Select a relevant project walkthrough

  Prepare a brief walkthrough of an LLM application you have built, if available: the use case, your contribution, Python implementation, LLM service or framework used, deployment approach, and results. The role calls for building and deploying GenAI applications.

• Practice a RAG design exercise

  Sketch a chatbot or copilot that ingests documents, creates embeddings, retrieves from a vector database, and generates answers. Explain how you would assess retrieval quality and choose between options such as FAISS, Pinecone, and Weaviate; these are examples named in the posting.

• Show how you improve prompts and outputs

  Prepare a before-and-after prompt example and a small evaluation plan with test inputs, failure cases, and quality measures. Be ready to explain when fine-tuning might be warranted; the posting says to fine-tune models where required.

• Review Python and backend evidence

  Choose a code sample or architecture diagram, if available, that demonstrates Python, an LLM API or framework, and a REST API or microservice. Note your specific decisions rather than implying experience with every tool listed.

• Rehearse production trade-offs

  For your design, outline how you would address latency, scalability, security, and performance, and how you would work with AI/ML, data, and product teams. These are stated responsibilities; your proposed approach is preparation, not an employer-specified process.

• Prepare evidence for optional experience

  If applicable, gather concise examples of open-source model fine-tuning, cloud deployment, MLOps or LLMOps, Docker or Kubernetes, and data privacy or AI security. The posting labels these good-to-have, not required.

• Draft questions for Eazygurus

  Ask which GenAI use cases the role will prioritize, how LLM and RAG solutions are evaluated, what deployment constraints matter most, and how the AI/ML, data, and product teams collaborate.