AI Engineer

What matters most

• Production-Ready AI Integration

  The role emphasizes building scalable, secure, and reliable AI applications. You must demonstrate how you move beyond prototypes to deploy LLM integrations and agentic workflows that meet enterprise-grade performance and security standards.

• Agentic Workflow Orchestration

  A core requirement is developing complex agentic workflows using frameworks like LangChain or LlamaIndex. You should be prepared to discuss how you design multi-step agent logic, manage state, and ensure interoperability using standards like the Model Context Protocol.

• RAG Pipeline Optimization

  You are expected to design and refine RAG systems, specifically focusing on the technical stack of embeddings, vector databases, and retrieval logic. Prioritize explaining how you improve response grounding and accuracy in high-stakes environments.

• Backend Engineering for AI

  The role requires strong software engineering fundamentals, specifically Python and asynchronous programming with FastAPI. You must show how you integrate AI services into existing backend architectures while maintaining system stability and observability.

Likely challenges to think through

• Balancing Innovation with Governance

  As a fintech company, Fiserv likely requires strict adherence to security and governance. The challenge is implementing cutting-edge AI features while ensuring they comply with enterprise safety, data privacy, and reliability requirements.

• Managing Non-Deterministic AI Behavior

  Integrating LLMs into production systems introduces unpredictability. Prepare to discuss your strategies for AI observability, monitoring, and testing to ensure consistent outputs and mitigate risks in automated workflows.

• Technical Debt in Rapidly Evolving Frameworks

  The AI ecosystem, including orchestration tools and protocols, changes rapidly. You will need to demonstrate how you maintain long-term system maintainability while adopting new tools like Cursor or evolving standards like MCP.

Topics to prepare

• Agentic Workflow Orchestration

  Review the conceptual differences between simple LLM chains and autonomous agentic workflows. Exercise: Design a multi-agent architecture using LangChain or LlamaIndex that handles a complex, multi-step task like automated document processing. This is directly relevant to the requirement for building agentic workflows to improve business outcomes.

• RAG Pipeline Optimization

  Study advanced retrieval strategies beyond basic similarity search, such as hybrid search, re-ranking, and query expansion. Exercise: Create a diagram or pseudocode for a RAG pipeline that includes specific grounding techniques to minimize hallucinations. This aligns with the role's focus on optimizing RAG pipelines and ensuring response reliability.

• AI Observability and Governance

  Research standard metrics for monitoring LLM performance in production, including latency, cost, token usage, and output quality. Exercise: Define a monitoring strategy for an AI service that tracks drift and security vulnerabilities. This is highly relevant to the preferred qualification for AI observability and production-ready AI safety.

Skills to revise

• Asynchronous Python with FastAPI

  Practice building non-blocking API endpoints using Python's asyncio library and FastAPI. Exercise: Develop a sample FastAPI service that calls an external LLM API asynchronously to handle concurrent requests efficiently. This is a core requirement for delivering scalable AI services at Fiserv.

• Model Context Protocol (MCP) Implementation

  Familiarize yourself with the Model Context Protocol specification for standardizing how AI models access external data and tools. Exercise: Build a simple MCP server that exposes a local data source to an LLM client. This is a specific preferred skill mentioned for improving context handling and interoperability.

• Vector Database Management

  Gain hands-on experience with vector database operations, including indexing, metadata filtering, and CRUD operations for embeddings. Exercise: Implement a script to ingest, chunk, and store documents into a vector store, then perform a filtered retrieval. This is essential for the RAG pipeline development responsibilities.

• Build a RAG pipeline project using FastAPI

  Develop a backend service in Python using FastAPI that implements a RAG pipeline. Include logic for document embedding, vector store integration, and response grounding to demonstrate your ability to build production-ready AI services.

• Demonstrate agentic workflow orchestration

  Create a project using LangChain, LlamaIndex, or CrewAI that showcases an agentic workflow. Focus on how you manage state, tool usage, and multi-step reasoning to solve a specific business-oriented problem.

• Prepare examples of AI observability and testing

  Document your experience with monitoring, evaluating, and testing LLM-based applications. Be ready to discuss how you ensure reliability, performance, and security in production environments, including how you handle model behavior testing.

• Formulate questions about Fiserv's AI infrastructure

  Prepare thoughtful questions regarding their current stack, such as how they manage Model Context Protocol (MCP) integrations, their approach to AI governance in a fintech context, or how they balance security with the agility of agentic workflows.