S&C Global Network - AI - Hi Tech - Data Science Associate 2

What matters most

• Generative AI and RAG Implementation

  The role centers on building and validating LLM-powered applications. You must demonstrate the ability to prepare knowledge sources for Retrieval-Augmented Generation (RAG) and assist in the development of agentic workflows using frameworks like LangChain or LangGraph.

• Python-Based AI Engineering

  You are expected to develop Python components that integrate with APIs and enterprise systems. This requires a strong grasp of software engineering practices, including source code management with Git and the ability to write clean, maintainable code for AI solutions.

• Functional Testing and Validation

  A core responsibility is the rigorous testing of AI outputs against defined test cases. You must be prepared to analyze model responses, identify performance gaps, and document findings to ensure the reliability of AI-powered solutions before deployment.

• Cloud Platform Integration

  You will support environment configuration and monitoring on Azure or AWS. Familiarity with cloud-native AI services and enterprise security standards is essential for successfully deploying and maintaining applications in a production-ready environment.

Likely challenges to think through

• Balancing Rapid Prototyping with Enterprise Standards

  As an associate, you will likely work on fast-paced AI projects while needing to adhere to strict enterprise security and documentation requirements. Prepare by practicing how to maintain high-quality technical documentation and version control while iterating quickly on AI prototypes.

• Navigating Ambiguity in AI Output Validation

  Validating Generative AI outputs is inherently difficult due to their non-deterministic nature. You may face challenges in defining clear success metrics for conversational agents. Prepare by studying evaluation frameworks for LLMs and how to systematically test for hallucinations or bias.

Topics to prepare

• Retrieval-Augmented Generation (RAG) Architecture

  Understand the end-to-end flow of RAG, including document ingestion, chunking, embedding, vector database storage, and retrieval. Exercise: Create a diagram illustrating how a user query triggers a vector search and how the retrieved context is injected into an LLM prompt. This is relevant as the role requires preparing and validating knowledge sources for RAG solutions.

• Agentic AI and Workflow Orchestration

  Study the concept of agentic AI, specifically how agents use tool calling to perform multi-step tasks. Exercise: Review the documentation for LangGraph or similar frameworks to understand how to define stateful workflows and decision-making loops. This is relevant because the role involves assisting in building and testing agent workflows.

• Prompt Engineering Best Practices

  Learn techniques for optimizing LLM outputs, such as few-shot prompting, chain-of-thought, and system instructions. Exercise: Take a specific business task and write three different prompts, then compare the quality of the generated outputs. This is relevant as the role requires supporting LLM-powered application development using prompts.

Skills to revise

• Python for AI Integration

  Practice writing Python scripts that interact with REST APIs and handle JSON data structures. Exercise: Build a simple Python script that sends a request to an LLM API and parses the JSON response to extract specific information. This is directly required for developing Python-based components and API integrations.

• Version Control with Git

  Master basic Git commands for collaborative development, including branching, committing, and merging. Exercise: Create a local repository, make changes to a Python script, commit them, and push to a remote repository. This is relevant as the role requires using Git for source code management and collaboration.

• SQL for Data Retrieval

  Refine skills in writing SQL queries to extract and manipulate data from relational databases. Exercise: Write queries involving joins, filtering, and aggregation to prepare a dataset for analysis. This is a required skill for supporting data-driven AI solutions.

• Prepare a project walkthrough of an LLM-powered application

  Develop a small project using Python that demonstrates RAG or an agentic workflow. Be prepared to explain the business problem you solved, your specific code contributions, and the methodology used to test the AI output quality.

• Demonstrate proficiency in prompt engineering and API integration

  Create a portfolio example showing how you used prompt engineering to improve LLM responses and integrated a REST API to fetch external data. Document how you handled JSON data structures and error handling within your code.

• Review cloud AI and deployment fundamentals

  Familiarize yourself with the basic AI services offered on Azure or AWS. Prepare to discuss how you would use Git for version control and your understanding of the CI/CD pipeline in the context of deploying AI components.

• Articulate your knowledge of RAG and vector databases

  Be ready to explain the RAG architecture, specifically how you prepare and validate knowledge sources. If you have used vector databases or embeddings, prepare a summary of how you indexed data for efficient retrieval.

• Formulate technical questions for the interviewers

  Prepare questions regarding the team's current development lifecycle, such as how they manage environment configuration on cloud platforms or how they approach functional testing for conversational AI agents.