AI Engineer | Digital Products & AI

PA Consulting•Charlotte, NC

9h•Hybrid

About The Position

This practice is built for the way enterprise software actually gets made now: small senior pods, AI agents doing much of the build, and outcomes in weeks where traditional delivery approaches took months. We build digital products, AI agents, intelligent workflows and modernized platforms that help organizations operate differently across healthcare and life sciences, manufacturing, energy and utilities, transportation and consumer industries. None of it is pilots or demos. The solutions we build run in production and solve problems that matter to our clients' businesses. As an AI Engineer, you'll work directly with clients to shape, design and build those solutions. Working alongside Product Strategists and Data Engineers, you'll take products from discovery through deployment, combining technical depth with a practical understanding of what clients need to achieve. On some engagements, we continue to run what we've built, giving you the opportunity to stay close to the product and see the impact of your work long after go-live. This is a hands-on engineering role. Most of the code here is written by agents. That does not make this a supervision job. It moves the engineering upstream, where strong engineering judgment becomes even more important. You'll write code, review agent-generated outputs, and shape the architecture, controls and acceptance criteria that determine whether a solution succeeds in production. Requirements do not always arrive fully formed, and part of the role is helping define the problem while continuing to move delivery forward.

Requirements

You have a track record of designing, building, and delivering complex technology solutions in production environments.
Engineering foundations
Experience building and operating production software.
Client-facing technical delivery experience is expected at senior levels.
Strong software engineering skills, ideally including Python and TypeScript.
Experience designing and delivering cloud-native systems on AWS, Azure, or GCP.
AI engineering
Hands-on experience building AI applications and agent-based systems in production.
Experience with orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies, and the judgment to know when an orchestration layer earns its complexity.
Experience building retrieval-enabled systems and working with technologies such as pgvector, Elasticsearch, or OpenSearch, with an understanding of the trade-offs between retrieval quality, latency, and cost.
Enterprise delivery
Experience integrating applications with enterprise systems, data platforms, APIs, identity providers, and MCP-enabled services.
Strong technical judgment, communication skills, and the ability to navigate ambiguity while working directly with clients and multidisciplinary teams, balancing delivery speed with quality, security, and long-term maintainability.

Nice To Haves

Experience delivering solutions within consulting, professional services, or time as a technical founder.
Experience in healthcare and life sciences, manufacturing, energy and utilities, transportation, or consumer industries.
Experience building reusable platforms, tooling, or AI solutions in regulated environments.

Responsibilities

Architect the elements that determine whether AI solutions succeed in production, including tool selection, memory and context management, guardrail design, and human-in-the-loop controls.
Build and deploy the technical components that power agentic systems, including retrieval pipelines, MCP services and integrations, and the systems that connect AI capabilities to enterprise data and processes.
Modernize platforms and applications around current business needs, rebuilding where necessary rather than simply moving legacy functionality onto newer technology.
Design security and operational resilience into solutions from the start, including access controls, identity management, deployment guardrails, and oversight of automated actions.
Break down complex problems into deliverable components, helping shape scope, sequencing, and implementation approaches that enable rapid delivery.
Create reusable patterns, skills, and tooling that strengthen future engagements and contribute to the growth of the capability.