Senior AI Engineer

CDW

21d

About The Position

We are seeking a Senior AI Engineer to transition our AI capabilities from "prototype" to "production." In this role, you will not just experiment with models; 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’s 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

Requirements

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: LangChain, LangGraph, 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 can be 100% useful. Excellent communication skills, with experience in documenting technical designs, sharing insights, and enabling team knowledge transfer.

Responsibilities

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, ServiceNow etc.)
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.