Generative Ai R&t Intern

2 weeks ago

Changi, Singapore Thales Full time

Location: Singapore, Singapore

In fast changing markets, customers worldwide rely on Thales. Thales is a business where brilliant people from all over the world come together to share ideas and inspire each other. In aerospace, transportation, defence, security and space, our architects design innovative solutions that make our tomorrow's possible.

Thales established its presence in Singapore in 1973 to support the expansion of aerospace-related activities in the Asia-Pacific region. Throughout the last four decades, the company grew from strength to strength and is today involved in the primary businesses of Aerospace (including Air Traffic Management), Defence & Security, Ground Transportation and Digital Identity & Security. Thales today employs over 2,100 people in Singapore across all its business areas.

**Description**:
The objective of the project is to solve industry problems in aviation, security and defense using language-model-powered Retrieval-Augmented Generation (RAG) & Agentic AI*. The end goals include proof-of-concept (POC) demonstrators to business partners. The challenges are to tailor the system to the unique business requirements, as well as to keep up with the breakneck pace of breakthroughs in LLM. The work involves entire machine-learning lifecycle including data exploration, data preparation (cleaning/transforming/annotation), and model serving/testing/tuning.

**Responsibilities**:

- Right-size the computing hardware / software for the jobs
- Collect / clean / transform / annotate data
- Develop software codes to automate entire machine-learning lifecycle

**Requirements**:

- Hands-on experience with machine learning
- Proficient in at least two programming languages e.g. shell, Python, R, Matlab, C
- Preferably experience with Generative AI, especially using API calls with chat completion, vector embedding and function calling
- Preferably experience in making, serving and proxying API calls e.g. HTTP
- Keen interests in machine-learning operations for scale up and fast iteration
- Team player

**Expected Outcome**:

- Demonstration of a working system with well-defined end-to-end test scenarios
- Documentation of the finding, design and test cases / results
- Team sharing and communication of various forms, including but not limited to live demo, video and workshops

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