Artificial Intelligence Engineer - Generative AI
First seen by Alion on Oct 13, 2026.
Total Experience: 8-13 years
Role & Responsibilities:
We are seeking a highly experienced AI Evangelist & Core AI Engineer who combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise.
This is not primarily an advisory role. The successful candidate will be expected to design, build, demonstrate, deploy, and scale real AI solutions, while simultaneously acting as an internal AI champion who helps engineering teams, business leaders, architects, and delivery organizations understand how emerging AI technologies can create measurable business value.
Key Responsibilities:
1. Core AI Engineering & Architecture:
- Design, develop, and implement enterprise-grade AI and Generative AI applications.
- Build scalable applications using LLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms.
- Architect AI solutions capable of supporting enterprise-scale workloads with considerations for scalability, reliability, security, performance, observability, cost optimization, and governance.
- Develop and implement advanced Retrieval-Augmented Generation (RAG) architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding.
- Build multi-agent and agentic AI applications capable of reasoning, tool usage, workflow execution, orchestration, and enterprise system integration.
2. Generative AI & LLM Engineering:
- Build production-grade applications utilizing leading commercial and open-source LLM ecosystems.
- Design scalable LLM platforms supporting multiple enterprise applications and use cases.
- Develop reusable LLM services, APIs, components, agent frameworks, and reference architectures.
3. AI Agents & Automation:
- Design and implement AI agents and agentic workflows that automate complex business and engineering processes.
- Build agents capable of interacting with enterprise applications, APIs, databases, documents, development environments, and workflow platforms.
4. Build Frontier AI Demonstrations:
- Build advanced AI prototypes and demonstrations before formal enterprise implementation.
- Experiment with emerging AI models, agents, frameworks, development tools, and architectures.
- Convert emerging technology into demonstrable business scenarios.
5. AI Evangelism & Enterprise Adoption:
- Act as a visible internal champion for responsible and effective AI adoption.
- Educate engineering and business teams about practical AI opportunities.
Mandatory Skills:
- Machine Learning, Artificial Intelligence Engineer, Deep Learning, Artificial Intelligence, Azure Cognitive Services, AI Solutions, AI Platform, AI Techniques, Artificial Intelligence Developer
Skills
Artificial Intelligence, Machine Learning, Generative AI, LLM, Agentic AI, Deep Learning, RAG, Azure Cognitive Services