Sr Developer- AI

What matters most

• Production-Grade AI Backend Development

  The core of this role is designing and scaling backend services that integrate LLMs and ML models. You must demonstrate the ability to move beyond experimental code to build reliable, high-performance, and maintainable production systems using Python frameworks like FastAPI, Flask, or Django.

• GCP Cloud-Native Implementation

  You are expected to architect and deploy services specifically within the Google Cloud Platform ecosystem. Proficiency in managing infrastructure via Cloud Run, GKE, and Vertex AI, while ensuring scalability, security, and proper IAM configuration, is essential for this role.

• AI Orchestration and RAG Pipelines

  The role requires implementing Retrieval-Augmented Generation (RAG) workflows and managing AI orchestration. You should be prepared to discuss how you handle data pipelines, vector databases, and the integration of embeddings to ensure accurate and efficient AI model responses.

• Cross-Functional Engineering Collaboration

  Success depends on working closely with data scientists, AI engineers, and DevOps teams. You must be able to translate complex AI requirements into scalable backend architecture while participating in Agile ceremonies and maintaining high standards for code quality and peer reviews.

Likely challenges to think through

• Optimizing AI Inference Latency

  Balancing the high computational cost of LLMs with the need for low-latency, real-time API responses is a significant technical hurdle. Prepare by reviewing strategies for model optimization, caching, and asynchronous processing in Python to maintain performance under load.

• Managing MLOps and CI/CD Complexity

  Integrating AI models into standard CI/CD pipelines introduces unique challenges regarding versioning, data drift, and model deployment. You should be ready to discuss how you manage the lifecycle of containerized AI applications using Docker and Kubernetes in a production environment.

Topics to prepare

• RAG Pipeline Architecture

  Review the conceptual flow of Retrieval-Augmented Generation, specifically how data is ingested, embedded, stored in vector databases, and retrieved to augment LLM responses. Exercise: Design a high-level architecture diagram for a RAG system that handles document ingestion and real-time query retrieval, focusing on latency optimization.

• Microservices and Asynchronous Programming

  Understand the principles of building scalable, decoupled backend services using Python. Focus on how asynchronous programming (asyncio) improves throughput in I/O-bound AI applications. Exercise: Refactor a synchronous API endpoint into an asynchronous one using FastAPI to demonstrate improved concurrency.

• MLOps and Production AI Lifecycle

  Study the lifecycle of AI models in production, including versioning, monitoring, and CI/CD for ML. Exercise: Outline a CI/CD pipeline strategy for deploying a model to GCP Vertex AI, including automated testing and monitoring for model drift.

Skills to revise

• GCP Cloud-Native Deployment

  Gain proficiency in deploying containerized applications to Google Cloud Run and GKE. Exercise: Create a Dockerfile for a Python-based AI service and write a deployment configuration for Google Cloud Run, ensuring proper IAM and environment isolation.

• Python AI Framework Integration

  Develop hands-on experience integrating LLMs using frameworks like LangChain or Hugging Face. Exercise: Build a simple Python script that uses LangChain to chain a prompt with a vector database retrieval step, simulating a production-grade orchestration workflow.

• SQL and NoSQL Database Management

  Practice data modeling and querying for AI-driven applications using SQL and NoSQL databases. Exercise: Write optimized SQL queries for BigQuery to extract and preprocess data for model training or inference, and compare this with a NoSQL schema design for storing unstructured metadata.

• Architect a RAG Pipeline Prototype

  Develop a small-scale project using Python, LangChain, and a vector database (like FAISS or Pinecone) to demonstrate your ability to integrate LLMs with external data sources. Document the design choices regarding latency and retrieval accuracy.

• Map GCP Services to AI Workflows

  Prepare a technical summary of how you would utilize specific GCP services—such as Vertex AI, Cloud Run, and BigQuery—to build a scalable, production-grade AI backend. Focus on how you ensure fault tolerance and monitoring.

• Review Microservices and Async Patterns

  Refresh your knowledge of asynchronous programming in Python (asyncio) and RESTful API design. Be ready to discuss how you handle data validation and service communication in a distributed, containerized environment using Docker and Kubernetes.

• Prepare MLOps and CI/CD Examples

  Draft a walkthrough of a past project where you implemented CI/CD pipelines for AI models. Highlight how you managed model versioning, deployment, and performance observability in a production setting.

• Formulate Strategic Questions for the Team

  Prepare questions regarding the current AI infrastructure at Mattel, such as how they prioritize model inference optimization or how cross-functional teams (data scientists and DevOps) collaborate on the deployment lifecycle.