Job Overview

TE is looking for a technically strong and motivated Agentic AI Intern to join our Corporate Technology Center (CTC) in Harrisburg. You will be working closely with a newly formed AI team that is building TE’s AI Hub in Singapore – remotely 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 centers 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.
 

Duties & 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 MLOps practices.
•    Contribute to internal documentation, technical demos, and knowledge-sharing sessions to communicate AI platform progress to cross-functional teams and stakeholders.
•    Support validation of AI outputs and assist with knowledge transfer activities in collaboration with local Singapore research institutes and university partners.
 

Job Requirements

Required Qualifications:
  
•    Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical field.
•    Solid programming skills in Python, including experience writing structured, readable, and maintainable code.
•    Good understanding of large language models (LLMs) – how they work, their capabilities, and their limitations – and familiarity with core agentic AI concepts such as tool use, memory, planning, and multi-agent coordination.
•    Hands-on experience with at least one agentic or LLM framework (e.g. LangChain, LlamaIndex, AutoGen, CrewAI) through coursework, personal projects, or open-source contributions.
•    Familiarity with software engineering fundamentals: version control (Git), modular code design, debugging, and basic testing practices.
•    Strong analytical mindset with the ability to design experiments, interpret results, and communicate findings clearly to both technical and non-technical audiences.
•    Keen interest in R&D, a willingness to work with ambiguous problems, and the drive to learn new tools and techniques quickly.
 

Preferred:
 
•    Experience building RAG systems and working with vector databases such as FAISS, Chroma, Pinecone, or Weaviate.
•    Familiarity with model evaluation techniques including LLM-as-a-judge, RAGAS, or task-specific benchmarking frameworks.
•    Exposure to cloud platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI) and tools such as Docker or basic CI/CD workflows.
•    Knowledge of responsible AI practices including hallucination mitigation, output validation, agent guardrails, and safe deployment considerations.
•    Experience with ML or deep learning libraries (PyTorch, Hugging Face Transformers) and an understanding of model fine-tuning or adaptation techniques.
•    Interest in or basic knowledge of engineering domains such as materials science, manufacturing processes, or product design – helpful but not essential.
•    Familiarity with TE products such as connectors, cables, or sensors is a plus.
 
What You Will Gain:
 
•    Hands-on experience designing and deploying real agentic AI systems in a corporate R&D environment.
•    Exposure to how frontier AI is applied to complex industrial engineering challenges at global scale.
•    Mentorship from experienced AI scientists and engineers, with regular feedback and guidance.
•    Opportunities to collaborate with Singapore’s leading research institutes and universities.
•    The chance to contribute work that directly shapes TE’s AI platform strategy and capabilities.

Competencies