Agentic AI Intern (Hybrid) - Able to Commit 5 - 6 Months

Start apply with LinkedIn

Start

Please wait...

Agentic AI Intern (Hybrid) - Able to Commit 5 - 6 Months

8/19/26

At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.

Tyco Electronics Singapore Pte Ltd (TE Connectivity) is looking for a technically strong and motivated Agentic AI Intern to join our Corporate R&D Center. You will be embedded in a newly formed AI team that is building TE’s AI Hub in Singapore – working on real problems at the intersection of frontier AI and industrial engineering.   This is not a passive internship. You will design and build agentic AI systems, run experiments, and contribute directly to a platform used by engineers across TE’s global business units. You will collaborate with R&D scientists, business unit partners, and local university and government research centres to embed AI into TE’s design and process development workflows.   We are looking for interns who are genuinely passionate about agentic AI and software engineering – whether that passion comes from coursework, personal projects, research, or open-source contributions. If you have strong foundations and the drive to learn fast, we encourage you to apply.   Job Responsibilities

Design, implement, and evaluate agentic AI workflows using orchestration frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI, targeting engineering use cases in product design and manufacturing process development. Build and test multi-agent systems incorporating tool-use, retrieval-augmented generation (RAG), memory management, and multi-step planning to solve complex, domain-specific engineering tasks. Develop and integrate RAG pipelines with vector databases (e.g. FAISS, Chroma, Weaviate) to enable semantic search and knowledge retrieval over technical documents and engineering data. Benchmark and evaluate state-of-the-art LLMs (e.g. GPT-4o, Claude, Gemini, Llama, Mistral) for engineering domain tasks, applying structured evaluation methods to assess accuracy, reliability, and safety. Apply prompt engineering techniques – including few-shot prompting, chain-of-thought, and structured output generation – to improve agent task performance and output quality. Write clean, well-documented, and testable Python code; contribute to shared codebases and follow software engineering best practices including version control (Git) and code review. Assist with deploying and integrating AI agents into cloud environments (AWS, Azure, or GCP), working with APIs, containerisation tools, and basic