Principal AI Engineer
Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Jul 28, 2026. Magnit scores B on the Alion truth index.
Principal AI Engineer
About the Role
We’re hiring a Principal AI Engineer to lead how we design, build, and productize generative and agentic AI, both inside our SaaS platform and across our products. This is a hands-on technical leadership role: you’ll own the architecture for production AI agents, set the standards teams follow to take AI from prototype to production, and mentor engineers along the way.
The hard problems here aren’t model demos. They’re reliable, safe, observable agent systems running in production: orchestration, memory, guardrails, evaluation, and human-in-the-loop control. You’ll be the person we trust to define what good looks like for AI engineering, and to make sure we build it responsibly.
What You’ll Do
Own the architecture for generative and agentic AI across our SaaS platform, and stay hands-on in the most complex, critical components.
Design and ship multi-agent systems that reason, plan, and execute, including the agentic loops, control flow, and failure isolation that keep them reliable in production.
Build agent harnesses, reusable agent skills/tools, and MCP servers as the standard tool and integration layer between models and our systems, APIs, and data.
Architect RAG systems (including GraphRAG) that ground outputs in trusted data, and event-driven pipelines that trigger and coordinate agents at scale.
Productize agents end-to-end with guardrails, confidence-score-based routing, human-in-the-loop state machines, evaluation harnesses, tracing, and circuit breakers.
Embed responsible-AI practices (privacy, bias mitigation, and auditability) directly into the reasoning and execution loops.
Set architecture and code-quality standards, run design and code reviews, and mentor engineers; steer teams toward the right technologies and away from hype-driven choices.
Make pragmatic build/buy decisions across frameworks, model providers, and orchestration patterns, balancing reliability, latency, security, and cost.
Build with AI coding tools and AI-assisted development to raise team velocity and quality, with disciplined review gates.
What You’ll Bring
10+ years in software engineering, with a strong track record shipping production SaaS and large-scale, cloud-native distributed systems (AWS, GCP, or Azure).
5+ years in technical leadership (Staff / Principal / Lead / Architect) driving cross-team decisions and mentoring engineers.
Hands-on experience building GenAI applications: LLMs, prompt and context engineering, embeddings, vector databases, and RAG.
Proven experience designing and shipping agentic AI systems: multi-agent orchestration, tool/function calling, agentic loops, memory, and autonomous execution, both embedded in a product and standalone.
Depth in AI productization: guardrails, confidence-based logic, human-in-the-loop control, evaluation, observability, and reliability of agents in production.
Experience building MCP servers (or equivalent tool layers), reusable agent skills, and event-driven architectures.
Strong Python plus one of Go, Java, or TypeScript; fluency with modern AI and agent frameworks (e.g., LangGraph, LangChain, CrewAI, AutoGen).
Practical experience building products with AI coding tools.
Working knowledge of responsible AI: privacy, bias, and auditability.
Nice to Have
Building, fine-tuning, and deploying Small Language Models (SLMs) for routing, extraction, classification, and tool-calling, including on-device/edge and privacy-sensitive deployments.
MLOps / LLMOps experience and observability/tracing stacks (e.g., OpenTelemetry).
Open-source contributions, patents, or publications in AI.
MS or PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).