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

What matters most

• Development of LLM and RAG Applications

  The core responsibility is building and validating Generative AI solutions. This requires proficiency in Python, prompt engineering, and API integration to create functional applications. You must be prepared to demonstrate how you have prepared knowledge sources for RAG and how you have tested the outputs of these models.

• Cloud-Based AI Integration and Deployment

  The role requires working within Azure or AWS environments to support AI service configuration and monitoring. You must be able to apply standard software engineering practices, including Git for version control and CI/CD workflows, to ensure that AI components are effectively integrated into enterprise systems.

• Technical Documentation and Quality Validation

  Beyond coding, you are expected to perform functional testing, validate AI responses against defined test cases, and maintain clear technical documentation. This ensures that the solutions developed under senior guidance meet enterprise standards for accuracy and reliability.

Likely challenges to think through

• Balancing Rapid Prototyping with Enterprise Standards

  As an entry-level associate, you will likely work with fast-moving frameworks like LangChain or LangGraph. The challenge is maintaining high-quality, secure, and documented code while experimenting with these emerging technologies. Prepare by practicing how to translate experimental AI prototypes into robust, production-ready code.

• Navigating Complex Knowledge Retrieval Systems

  RAG solutions require not just coding, but the careful preparation and validation of data sources. You may face difficulties in ensuring data quality and relevance for retrieval. Prepare by understanding how vector databases and embeddings function to bridge the gap between raw data and LLM performance.

Topics to prepare

• Retrieval-Augmented Generation (RAG) Architecture

  Review the end-to-end RAG pipeline, specifically focusing on how knowledge sources are prepared, chunked, and embedded for retrieval. Exercise: Create a diagram illustrating the flow from raw document ingestion to LLM response generation, identifying where validation occurs. This is critical as the role requires preparing and validating knowledge sources for client solutions.

• Agentic AI and Workflow Orchestration

  Understand the conceptual difference between simple LLM prompts and agentic workflows that utilize tool calling. Exercise: Define a simple agent workflow where an LLM decides which tool (e.g., a calculator or a database query) to call based on user input. This aligns with the requirement to assist in building conversational AI and agent workflows.

Skills to revise

• Prompt Engineering and API Integration

  Practice crafting system prompts and managing context windows for LLM interactions. Exercise: Build a small Python script that interacts with a public LLM API to perform a specific task, such as summarizing text or extracting entities from JSON data. This directly supports the core responsibility of developing LLM-powered applications.

• LangChain/LangGraph Frameworks

  Gain familiarity with building chains and stateful graphs for AI applications. Exercise: Use LangChain to create a basic chain that connects a prompt template to an LLM and processes the output. This is a preferred skill for the role and essential for modern AI application development.

• SQL and Data Validation

  Ensure proficiency in writing complex SQL queries for data retrieval and validation. Exercise: Write a SQL query to join multiple tables and perform data cleaning or validation checks on a sample dataset. This is necessary for the role's focus on preparing and validating knowledge sources for AI solutions.

• Version Control with Git

  Practice standard software development lifecycle (SDLC) practices. Exercise: Create a local repository, commit changes, and practice branching and merging workflows. This is required for the collaborative development environment described in the job posting.

• Prepare a project walkthrough for RAG or LLM applications

  Develop a concise explanation of a past project involving LLMs or data science. Be ready to detail the business problem, your specific technical contribution, the tools used (e.g., Python, APIs), and your methodology for testing and validating the solution.

• Demonstrate proficiency in Generative AI and Prompt Engineering

  Create a portfolio of prompt engineering examples that show how you iterate on prompts to improve LLM output quality. Be prepared to discuss how you handle context, system instructions, and output formatting using JSON or other structured data.

• Review core concepts of Agentic AI and RAG frameworks

  Study the fundamentals of Retrieval-Augmented Generation (RAG) and agentic workflows. Familiarize yourself with how LangChain or LangGraph can be used to orchestrate tool calling and knowledge retrieval from vector databases.

• Practice technical skills in Python, SQL, and API integration

  Ensure you can write clean, documented Python code and perform complex SQL queries. Practice building or consuming REST APIs, as this is a core requirement for integrating AI models into enterprise systems.

• Prepare for cloud and deployment-related questions

  Brush up on basic concepts for Azure or AWS AI services. Be ready to explain your experience or understanding of the software development lifecycle, including version control with Git and the role of Docker and CI/CD in deploying AI applications.

• Formulate thoughtful questions for the interviewers

  Prepare questions regarding the team's current approach to AI solution delivery, how they handle data validation for RAG pipelines, or how junior associates are mentored in cloud-based AI development environments.