Custom Software Engineer

What matters most

• Enterprise-Grade GenAI Development

  The role centers on building scalable AI applications using LLMs, RAG pipelines, and agentic workflows. You must demonstrate the ability to move beyond prototypes to production-ready systems that integrate with existing enterprise architectures, ensuring reliability and performance.

• Advanced Python and AI Framework Proficiency

  With a requirement of 4+ years in Python and 2+ years in GenAI, you must be adept at using modern frameworks like LangChain, LangGraph, and LlamaIndex. Prioritize demonstrating deep technical knowledge in data modeling, vector databases, and semantic search optimization.

• Scalable Data Pipeline Engineering

  Beyond AI modeling, you are responsible for designing data ingestion and processing pipelines. You must show competence in handling large datasets using tools like Pandas and NumPy while ensuring these pipelines are robust enough to support real-time AI inference.

• System Observability and Maintenance

  The role requires monitoring, troubleshooting, and optimizing AI application performance using tools like Grafana. You must prioritize the ability to maintain system health and ensure that AI outputs remain accurate and performant within a production environment.

Likely challenges to think through

• Balancing AI Innovation with Enterprise Constraints

  Integrating cutting-edge LLMs into rigid enterprise environments often creates friction regarding security, authentication, and legacy system compatibility. Prepare by discussing how you manage trade-offs between model flexibility and strict enterprise security standards like OAuth and SSO.

• Optimizing RAG and Semantic Search Accuracy

  Developing RAG pipelines involves the inherent challenge of managing context windows and retrieval accuracy. Prepare by explaining your methodology for evaluating embedding models and vector search performance to minimize hallucinations and ensure relevant retrieval.

• Managing Agentic AI Complexity

  Agentic workflows introduce non-deterministic behavior, making them difficult to test and debug compared to traditional software. Prepare by discussing your approach to unit testing, observability, and guardrails for autonomous agents to ensure predictable outcomes.

Topics to prepare

• RAG Pipeline Architecture

  Focus on the end-to-end flow of Retrieval-Augmented Generation, including document chunking, embedding generation, and retrieval strategies. Exercise: Design a system architecture diagram that handles document ingestion, vector storage, and context retrieval for a Text-to-SQL application to demonstrate understanding of scalability.

• Agentic AI Workflows

  Understand the conceptual framework of autonomous agents that use tools to complete tasks. Exercise: Review the LangGraph documentation to explain how state management and cyclic graphs enable agents to perform multi-step reasoning and tool execution, which is critical for the role's focus on agentic workflows.

• Microservices and Enterprise Integration

  Study how AI models are integrated into larger enterprise systems using APIs and authentication protocols. Exercise: Outline a plan for deploying a Python-based AI service into a microservices architecture, focusing on how to handle JWT authentication and secure communication between services.

Skills to revise

• Advanced Python for AI

  Demonstrate proficiency in Python beyond basic scripting, specifically for data manipulation and model integration. Exercise: Write a script using Pandas and NumPy to preprocess a dataset for embedding, then use a library like Sentence Transformers to generate and store vectors in a mock database.

• Vector Database Management

  Gain hands-on experience with vector search operations. Exercise: Set up a local instance of a vector database like FAISS or Qdrant, perform a semantic search query, and explain how you would optimize the retrieval latency for a production-scale application.

• Observability and Monitoring

  Learn to monitor AI system performance using tools like Grafana. Exercise: Create a dashboard configuration that tracks key metrics for an LLM-based application, such as token usage, latency per request, and error rates, to ensure system reliability.

• Prepare a RAG pipeline architecture walkthrough

  Create a technical diagram and explanation of a RAG system you have built. Be prepared to discuss your choice of vector database, embedding models, and how you handled retrieval optimization, chunking strategies, and hallucination mitigation.

• Demonstrate Agentic AI workflow design

  Prepare a code sample or architectural overview using LangGraph or similar frameworks to show how you manage multi-step agentic workflows, state management, and tool-calling capabilities for enterprise tasks.

• Review Python and software engineering fundamentals

  Refresh your knowledge of OOPs, design patterns, and unit testing in Python. Be ready to discuss how you structure scalable FastAPI or Flask applications and implement robust authentication and authorization (JWT/OAuth).

• Prepare for Text-to-SQL and data pipeline discussions

  Document your experience in designing data ingestion pipelines and Text-to-SQL systems. Focus on how you ensure data security, handle schema mapping, and optimize query performance for natural language interfaces.

• Formulate questions regarding enterprise AI deployment

  Prepare thoughtful questions for the interviewers about their current stack, such as how they handle observability with Grafana in production, their strategy for model evaluation, or how they manage CI/CD for LLM-based applications.