Agentic AI Intern (Hybrid) - Able to Commit 5 - 6 Months
TE Connectivity2 months ago
Singapore, SingaporeIntern
Responsibilities
- Design, implement, and evaluate agentic AI workflows for engineering use cases in product design and manufacturing process development.
- Build and test multi-agent systems with tool use, retrieval-augmented generation, memory management, and multi-step planning.
- Develop and integrate RAG pipelines with vector databases for semantic search over technical documents and engineering data.
- Benchmark and evaluate LLMs for engineering-domain tasks, assessing accuracy, reliability, and safety.
- Apply prompt engineering techniques such as few-shot prompting, chain-of-thought, and structured output generation.
- Write clean, documented, testable Python code and contribute to shared codebases through version control and code review.
- Assist with deploying and integrating AI agents into AWS, Azure, or GCP environments using APIs, containerization tools, and basic MLOps practices.
- Create internal documentation, technical demonstrations, and knowledge-sharing sessions for cross-functional stakeholders.
- Support AI-output validation and knowledge-transfer activities with Singapore research institutes and university partners.
Requirements
- Currently pursuing a bachelor’s or master’s degree in computer science, artificial intelligence, data science, software engineering, or a related technical field.
- Strong Python programming skills and experience writing structured, readable, and maintainable code.
- Understanding of LLMs, including their capabilities and limitations, and 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 through coursework, personal projects, or open-source contributions.
- Familiarity with version control, modular code design, debugging, and basic testing practices.
- Ability to design experiments, interpret results, and communicate findings to technical and non-technical audiences.
- Interest in R&D, comfort with ambiguous problems, and willingness to learn new tools and techniques quickly.
- Preferred experience with RAG systems and vector databases such as FAISS, Chroma, Pinecone, or Weaviate.
- Preferred familiarity with LLM evaluation methods including LLM-as-a-judge, RAGAS, or task-specific benchmarking frameworks.
- Preferred exposure to AWS Bedrock, Azure OpenAI, Google Vertex AI, Docker, or basic CI/CD workflows.
- Preferred knowledge of responsible AI practices, hallucination mitigation, output validation, agent guardrails, and safe deployment.
- Preferred experience with PyTorch or Hugging Face Transformers and model fine-tuning or adaptation techniques.
- Interest or basic knowledge of materials science, manufacturing processes, or product design is helpful; familiarity with TE connectors, cables, or sensors is a plus.
Benefits
- Hybrid internship requiring a 5–6 month commitment.
- Hands-on experience designing and deploying agentic AI systems in a corporate R&D environment.
- Exposure to applying frontier AI 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 research institutes and universities.
- Opportunity to contribute to TE’s AI platform strategy and capabilities.
Categories
About TE Connectivity
TE Connectivity designs and manufactures connectors, sensors, and interconnect systems used by automotive, industrial, energy, aerospace, data center, and medical OEMs. It sells these components and related engineering services to equipment makers and system integrators worldwide, supporting applications from e-mobility and factory automation to communications infrastructure. The company is publicly traded as TE Connectivity plc (NYSE: TEL) and was previously known as Tyco Electronics after its 2007 separation from Tyco International.