Artificial Intelligence Engineer

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.