About the Role
The AI Engineering Intern will contribute to the design and development of generative AI applications and agentic solutions for clients. Working alongside experienced engineers, consultants, and data scientists, this role offers hands-on exposure to the full AI development lifecycle—from early prototyping through to production-ready deployment. The position is focused on learning, with opportunities for mentorship, feedback, and increasing responsibility over time.
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Who You Will Work With
You will be part of the company's AI, Insights & Solutions (AIS) team, collaborating with multidisciplinary teams that include the company's consultants, product experts, designers, architects, engineers, and client stakeholders. The team works together to translate ambiguous business problems into robust AI applications that can be scaled and adopted.
What You’ll Do
- Contribute to the design and development of GenAI applications (e.g., copilots, workflow automation, decision support) using modern LLM stacks.
- Support the implementation of AI pipelines and components, including:
- Retrieval-Augmented Generation (RAG)
- Fine-tuning and parameter-efficient tuning
- Embedding generation and optimization
- Hybrid retrieval strategies (vector, graph, keyword)
- Assist in implementing tool use, function calling, and orchestration across AI workflows.
- Assist in building agent capabilities including context engineering, memory and state management, orchestration, and tool integration.
- Support model experimentation, evaluation design, and production deployment tasks alongside senior team members.
- Write clean, testable code and contribute to APIs and services through the full SDLC: build, test, deploy, monitor, iterate.
- Contribute to evaluation and observability for GenAI systems: regression test suites, automated scoring, and prompt or policy iteration loops.
- Gain exposure to responsible AI practices across system design, including:
- Implementing guardrails, fallbacks, and human-in-the-loop (HITL) workflows.
- Understanding the company's Responsible AI standards and supporting alignment in your work.
- Contribute to evaluation harnesses and reusable components that scale across client contexts.
- Contribute to ML solutions end-to-end, including:
- Data preparation, feature engineering, model selection, and training.
- Validation, testing, and performance analysis.
- Applying appropriate ML methods spanning classical ML and deep learning.
- Developing working knowledge of transformer fundamentals and LLM concepts.
- Collaborate with product, engineering, and data science teammates to build well-structured, reliable AI systems.
- Communicate clearly in working sessions, demos, and documentation—with both technical and non-technical audiences.
- Support client discussions and documentation as part of broader engagement teams.
About You
- 0–2+ years of professional AI/ML engineering experience, or equivalent through internships, research, academic projects, or open-source contributions.
- Currently pursuing or recently completed a bachelor’s, master’s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, or a related quantitative field; or equivalent early-career experience.
- Strong academic performance or demonstrated excellence through research, projects, internships, competitions, or open-source contributions.
- Proficiency in Python and some experience building APIs or services (e.g., REST/gRPC), or comparable project-based experience.
- Experience building complex, multi-stack generative AI programs from conception through production.
- Exposure to retrieval and search systems (e.g., vector search, hybrid retrieval, reranking) and familiarity with structured and unstructured data stores.
- Exposure to agentic patterns (context management, tool integration, orchestration, memory/state handling); hands-on experience preferred.
- Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.
- Excellent interpersonal and communication skills; interest in applying AI to real-world business problems in a client-facing environment.
- Fluency in English and Arabic is required.
Working Model & Travel
- This role requires a minimum of three days per week working together in person, either at a client location or at your company's home office.
- Travel may be required beyond your home office/primary working location. Frequency and destination vary by project needs.
