Senior AI Engineer

Overview

Description

At CDW, we make it happen, together. Trust, connection, and commitment are at the heart of how we work together to deliver for our customers. It's why we're coworkers, not just employees. Coworkers who genuinely believe in supporting our customers and one another. We collectively forge our path forward with a level of commitment that speaks to who we are and where we're headed.

We're proud to share our story and Make Amazing Happen at CDW.

At CDW, we make it happen, together. Trust, connection, and commitment are at the heart of how we work together to deliver for our customers. It's why we're coworkers, not just employees. Coworkers who genuinely believe in supporting our customers and one another. We collectively forge our path forward with a level of commitment that speaks to who we are and where we're headed.

We're proud to share our story and Make Amazing Happen at CDW.

Job summary

We are seeking a Senior AI Engineer to transition our AI capabilities from production-ready prototypes to scalable production systems. In this role, you will architect robust Agentic Systems that can plan, reason, and execute complex workflows autonomously for a wide variety of business user needs, while experimenting with cutting-edge models and tools to push the boundaries of what is possible.

The ideal candidate is a forward-thinking engineer who thrives on hands-on experimentation with AI models and frameworks, learns quickly, and is naturally curious. You will collaborate closely with product managers, business partners, and platform teams to deliver high-value AI agents that solve real-world problems across the enterprise.

What you will do

• Agentic Engineering & Orchestration
  • Workflow Design: Architect complex, multi-agent workflows using Microsoft AI tech stack. Design, develop and deploy agents to handle loops, interruptions, and human-in-the-loop interventions.
  • Tool Use & Function Calling: Build reliable tool layers that allow LLMs to safely interact with internal APIs, databases, and third-party SaaS platforms (e.g., Salesforce, Workday, Service Now).
  • State Management: Design persistence layers to manage agent memory, conversational history, and context windows efficiently.
• Advanced Data & RAG Strategy
  • Retrieval Pipelines: Build production-grade data retrieval and integration systems. Optimize vector indexing, document chunking, and re-ranking algorithms to ensure high-precision context retrieval.
  • Data Quality: Collaborate with Data Engineers to curate Golden Datasets for agent consumption.
• LLMOps, Evaluation & Quality
  • Automated Evaluation: Build CI/CD pipelines for AI that include “LLM-as-a-Judge” testing. Leverage frameworks to score agent outputs for accuracy, hallucination, and safety before deployment.
  • Observability: Instrument applications with tracing tools to visualize agent reasoning chains, monitor latency, and debug failures in production.
  • Cost Optimization: Monitor token usage and latency, optimizing prompt density and caching strategies to maintain high performance at sustainable costs.
• Innovation & Collaboration
  • Prototyping to Production: Rapidly validate new ideas using state-of-the-art models, then refactor successful prototypes into maintainable, tested production code.
  • Standards Adoption: Stay ahead of the curve by evaluating emerging technologies to standardize agent connectivity.

What we expect of you

• Core Engineering:
  Bachelor’s degree and 5 years of software engineering experience, with exposure to AI/ML applications OR 9 years of software engineering experience, with exposure to AI/ML applications
• AI Specialization: 2+ years specifically building with LLMs, with deep familiarity in:
  • Orchestration:
    Lang Chain, Lang Graph, or similar state-based frameworks.
  • Vector DBs:
    Pinecone, Weaviate, or pgvector.
  • Prompt Engineering:
    Advanced techniques (Chain-of-Thought, ReAct, Few-Shot).
• Production Mindset:
  Experience not just building demos, but operating them. You know how to handle rate limits, context window overflows, and non-deterministic errors.
• Soft Skills:
  
  Ability to explain probabilistic software to non-technical stakeholders, managing expectations that agents are never 100% accurate, but…