Principal AI Engineer
First seen by Alion on Jul 30, 2026.
Job Description :
This role sits between Senior AI Engineer and AI Solution Architect.
The Principal AI Engineer remains strongly execution-focused, but operates with broader technical scope, deeper system ownership, and higher accountability for quality, reliability, and outcomes.
You will lead by exampledesigning, coding, reviewing, and solving the hardest problemswhile shaping engineering patterns, mentoring others, and ensuring AI systems perform reliably in production.
Key Responsibilities :
1. Advanced Hands-On AI Engineering (Primary Focus) :
- Design, build, and operate complex production AI systems across GenAI, ML, agentic workflows, and hybrid architectures.
- Own implementation of :
1. Retrieval-augmented generation (RAG) pipelines.
2. Embeddings and vector search integrations.
3. Agent orchestration, tool calling, and decision logic.
4. Model inference, routing, caching, and optimization layers.
- Take responsibility for performance, correctness, scalability, and cost efficiency of AI workloads in production.
- Lead resolution of high-severity production issues involving AI behavior, data quality, latency, or system integration.
2. Technical Leadership & Design Authority :
- Serve as a technical authority on AI solution design and implementation details.
- Translate high-level solution architectures into precise, executable engineering designs.
- Make informed tradeoffs between speed, accuracy, cost, and maintainability.
- Drive consistent engineering quality through :
1. Code reviews.
2. Design reviews.
3. Reference implementations.
4. Engineering best practices.
3. AI System Hardening & Production Readiness :
- Ensure AI systems meet enterprise standards for :
1. Reliability and observability.
2. Security and data protection.
3. Monitoring, alerting, and rollback.
- Implement and evolve :
1. Evaluation pipelines (quality, grounding, hallucination detection).
2. Versioning strategies for prompts, models, and configurations.
3. Deployment strategies (blue-green, canary, controlled rollout).
- Partner with DevOps and Platform Engineering to ensure smooth CI/CD and runtime operations.
4. Mentorship & Engineering Enablement :
- Mentor Senior and mid-level AI Engineers through :
1. Pair programming.
2. Design walkthroughs.
3. Code reviews.
- Raise the technical bar by establishing clear patterns and expectations for AI engineering.
- Help translate evolving AI standards from Architecture into practical, repeatable implementations.
5. Cross-Functional Collaboration :
- Work closely with :
1. AI Solution Architects.
2. Product owners.
3. Business stakeholders.
- Contribute technical input during intake and solution shaping for new AI initiatives.
- Clearly communicate risks, constraints, and options to non-technical stakeholders when needed.
Essential Duties and Responsibilities :
- Include the following. Other duties may be assigned.
- 9+ years of software engineering experience with significant hands-on delivery of AI systems in production.
- Deep practical experience with :
1. GenAI and LLM-based systems.
2. RAG architectures and vector databases.
3. API-based and microservices architectures.
- Strong proficiency in Python and/or .NET.
- Experience integrating AI solutions with enterprise platforms (CRM, ERP, contact center, eCommerce).
- Solid understanding of MLOps / LLMOps concepts and production AI operations.
- Proven ability to independently own and deliver complex technical work.
Preferred Qualifications :
- Experience with agent orchestration frameworks (e.g., LangChain, LangGraph, Semantic Kernel, CrewAI).
- Experience with Azure AI services and cloud-native architectures.
- Background in distribution, MRO, pricing, supply chain, or customer experience domains.
- Experience operating AI systems with direct revenue, margin, or efficiency impact.
Skills
Artificial Intelligence, Solution Architect, Generative AI, Machine Learning, VectorDB, LLM, RAG, Python, MLOps, LangGraph, LangChain