Custom Software Engineer

What matters most

• Enterprise GenAI Application Development

  The core of this role is building scalable, production-ready AI solutions. You must demonstrate the ability to move beyond prototyping by integrating LLMs into enterprise workflows using frameworks like LangChain or LangGraph, while ensuring robust authentication, authorization, and API integration.

• RAG and Semantic Search Optimization

  A significant portion of the work involves optimizing Retrieval-Augmented Generation (RAG) pipelines and Text-to-SQL systems. You need to show expertise in managing vector databases, embedding models, and fine-tuning retrieval strategies to ensure high-quality, accurate outputs for enterprise data.

• Scalable Data Pipeline Engineering

  Beyond AI logic, you are responsible for designing data ingestion and processing pipelines. This requires strong Python skills, proficiency with data manipulation libraries like Pandas and NumPy, and an understanding of how to handle large-scale data flows within a microservices architecture.

• Performance Monitoring and Observability

  The role requires maintaining and troubleshooting AI applications in production. You must be prepared to discuss how you use tools like Grafana to monitor system performance, latency, and model reliability to ensure the stability of AI-powered features.

Likely challenges to think through

• Balancing Model Performance with Latency

  In enterprise RAG and Agentic workflows, there is a constant trade-off between the complexity of the LLM chain and response time. Prepare by discussing how you optimize retrieval steps, cache results, or choose appropriate embedding models to maintain a responsive user experience.

• Managing Non-Deterministic AI Outputs

  Integrating LLMs into enterprise systems introduces unpredictability. You may face difficulty ensuring consistent, secure, and accurate outputs. Prepare by explaining your approach to prompt engineering, guardrails, and rigorous unit testing for AI components.

• Adapting to Rapidly Evolving AI Tooling

  The stack includes diverse tools like LlamaIndex, MCP, and various cloud-based model providers. The challenge is maintaining deep expertise across a fragmented ecosystem. Prepare by demonstrating your ability to quickly learn new frameworks and your criteria for selecting the right tool for a specific business problem.

Topics to prepare

• RAG Pipeline Optimization

  Focus on the conceptual architecture of Retrieval-Augmented Generation, specifically how to improve retrieval accuracy through chunking strategies, hybrid search, and re-ranking. Exercise: Design a system architecture diagram for a RAG pipeline that handles document ingestion, vector storage, and query expansion to minimize hallucinations.

• Agentic AI Workflows

  Understand the theory behind autonomous agents, including planning, tool use, and memory management. Exercise: Review the LangGraph documentation to explain how state management and cyclic graphs differ from traditional linear LLM chains in solving complex, multi-step user queries.

• Microservices and Scalable Data Pipelines

  Study the principles of designing scalable, decoupled systems using microservices and efficient data ingestion. Exercise: Outline how you would design a Python-based service to ingest unstructured data, process it into embeddings, and store it in a vector database while ensuring high availability and performance monitoring.

Skills to revise

• Python Frameworks for AI Integration

  Proficiency in building APIs using FastAPI or Flask to serve AI models. Exercise: Create a simple FastAPI endpoint that accepts a natural language query, interacts with an LLM via LangChain, and returns a structured JSON response, including basic error handling and input validation.

• Vector Database Management

  Hands-on experience with vector stores like FAISS, Qdrant, or Weaviate. Exercise: Write a script to perform CRUD operations on a local vector database, focusing on how to efficiently query for semantic similarity using embedding models from Hugging Face.

• Text-to-SQL Implementation

  Developing systems that translate natural language into SQL queries. Exercise: Build a small prototype using LangChain to connect to a local SQLite database, where the LLM generates and executes SQL queries based on natural language prompts, ensuring secure parameterization to prevent injection.

• Architect a RAG Pipeline Walkthrough

  Prepare a technical deep-dive into a RAG system you have built. Focus on your choice of vector database (e.g., FAISS, Qdrant), embedding models, and how you handled retrieval optimization, chunking strategies, and latency reduction in a production environment.

• Demonstrate Agentic AI Workflow Proficiency

  Create a brief case study or code sample using LangChain or LangGraph to solve a multi-step task. Explain how you managed state, tool selection, and error handling within an agentic loop to ensure reliable outputs.

• Prepare Python and System Design Evidence

  Review your experience with FastAPI or Flask in microservices architectures. Be ready to discuss how you implement authentication (JWT/OAuth), unit testing, and CI/CD pipelines for Python-based AI services.

• Formulate Technical Questions for the Team

  Prepare specific questions regarding their current GenAI stack, such as how they handle model evaluation, their approach to monitoring AI performance with tools like Grafana, and how they manage data privacy when integrating enterprise APIs.

• Review Data Engineering and Text-to-SQL Skills

  Be prepared to explain your process for building scalable data ingestion pipelines using Pandas and NumPy, and describe your experience translating natural language queries into SQL, including how you handle schema mapping and query security.