Custom Software Engineer

What matters most

• Enterprise GenAI Application Development

  The core priority is building and maintaining production-grade AI solutions, specifically RAG pipelines, Text-to-SQL systems, and Agentic AI workflows. You must demonstrate the ability to move beyond prototypes to scalable, integrated enterprise software using Python and frameworks like LangChain or LangGraph.

• Scalable Data Pipeline Engineering

  You are expected to design and implement robust data ingestion and processing pipelines. This requires proficiency in handling data structures with Pandas and NumPy, managing vector databases, and ensuring efficient embedding generation for semantic search capabilities.

• System Architecture and Mentorship

  Beyond coding, the role requires contributing to high-level architecture decisions and mentoring team members. You must be prepared to justify design patterns, microservices integration, and observability strategies using tools like Grafana.

Likely challenges to think through

• Balancing AI Innovation with Enterprise Constraints

  Integrating cutting-edge LLMs and Agentic workflows into rigid enterprise environments often creates friction regarding security, authentication (JWT/OAuth), and latency. Prepare by discussing how you maintain performance and security standards while implementing experimental AI features.

• Optimizing RAG and Semantic Search Accuracy

  RAG pipelines often face challenges with retrieval quality and hallucination. The difficulty lies in fine-tuning embedding models and vector search parameters to meet specific business requirements. Prepare by explaining your methodology for evaluating and iterating on retrieval precision.

• Managing Complexity in Agentic Workflows

  Agentic AI introduces non-deterministic behavior, making debugging and monitoring significantly more complex than traditional software. Prepare by discussing how you use observability tools like Grafana and structured logging to troubleshoot complex AI decision-making chains.

Topics to prepare

• Retrieval-Augmented Generation (RAG) Architecture

  Review the end-to-end RAG lifecycle, specifically focusing on document chunking strategies, embedding model selection, and vector database retrieval optimization. Exercise: Design a system architecture diagram for a document-based Q&A bot that handles large-scale enterprise data, identifying potential bottlenecks in latency and retrieval accuracy.

• Agentic AI Workflows

  Understand the conceptual shift from static LLM chains to autonomous agents capable of tool use and multi-step reasoning. Exercise: Map out a workflow using LangGraph or similar frameworks where an agent must decide between querying a SQL database or searching a vector store to answer a complex user prompt.

• Microservices and Enterprise Integration

  Focus on the principles of decoupling AI components from core enterprise applications using APIs. Exercise: Draft a plan for containerizing a Python-based AI service using Docker and integrating it with an existing enterprise authentication system (JWT/OAuth) to ensure secure, scalable access.

Skills to revise

• Advanced Python for Data Pipelines

  Demonstrate proficiency in building scalable data processing pipelines using Pandas and NumPy. Exercise: Write a script to clean, tokenize, and batch-process a large dataset into embeddings, ensuring efficient memory management for high-volume data ingestion.

• Text-to-SQL Implementation

  Develop skills in mapping natural language queries to structured database queries. Exercise: Build a small prototype using LangChain that translates a natural language question into a valid SQL query for a relational database, including a mechanism to validate the generated SQL before execution.

• AI Observability and Monitoring

  Learn to monitor AI application performance using tools like Grafana. Exercise: Configure a mock dashboard to track key metrics for an LLM application, such as token usage, latency per request, and error rates for API calls to external models.

• Prepare a RAG and Agentic AI project walkthrough

  Develop a technical summary of a past project where you built a RAG pipeline or an Agentic workflow. Be prepared to explain your choice of vector database, embedding models, and how you handled data ingestion, retrieval accuracy, and latency optimization.

• Demonstrate Python and API design expertise

  Review your experience with FastAPI or Flask to build enterprise-grade APIs. Prepare to discuss how you implemented authentication (JWT/OAuth), data modeling, and unit testing within a microservices architecture.

• Articulate Text-to-SQL and LLM integration strategies

  Prepare to explain how you have integrated LLMs with enterprise data sources. Specifically, detail your approach to Text-to-SQL systems, including how you ensure query security, handle schema complexity, and validate generated SQL.

• Review CI/CD and observability practices

  Be ready to describe your experience with Git, CI/CD pipelines, and monitoring tools like Grafana. Explain how you have monitored the performance of AI applications in production to ensure reliability and scalability.

• Formulate questions on enterprise AI architecture

  Prepare thoughtful questions for the interviewers regarding their current stack, such as how they manage model versioning, their strategy for choosing between Azure OpenAI and AWS Bedrock, or how they handle data privacy in Agentic AI workflows.