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Job Description Summary
We are establishing a new AI capability hub in Singapore focusing on digital inspection for aero engine overhaul shops and component repair facilities. In addition to computer vision and NDT inspection, we see strong potential to use large language models (LLMs) and Generative AI (GenAI) to unlock value from e manuals, shop documents and other unstructured data.As a Data Scientist (LLM & GenAI), you will design and build LLM based solutions such as intelligent search and Q&A over technical manuals, automated extraction and structuring of shop information, and generation/summarization of inspection and repair reports. You will work closely with inspection engineers, shop operations and digital product teams to turn unstructured text into reliable, secure and scalable AI capabilities for our MRO network.
Job Description
Company Overview:
Are you ready to see your career take flight? At GE Aerospace, we believe the world works better when it flies. We are a world-leading provider of jet engines, components, and integrated systems for commercial and military aircraft. We have a relentless dedication to the future of safe and more sustainable flight and believe in our talented people to make it happen.
Site Overview:
For more than 40 years, our advanced facilities in Singapore have led aerospace innovation across the Asia Pacific region. From automating processes to leveraging smart factory technologies, robotics, and additive manufacturing, GE Aerospace is shaping the future of aviation. Responsible for more than 60% of our global repair volume, our Singapore site is the largest site for engine component maintenance, repair and overhaul work. We can’t do this work without talented people like you.
Join us and be part of our journey to innovate and excel in the aerospace industry.
Role Overview:
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Design and develop LLM/GenAI solutions for documentation‑driven use cases (search & Q&A, information extraction, summarization, report generation, assistant‑style tools for inspectors and engineers).
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Build document and RAG pipelines, including ingestion, processing and indexing of e‑manuals, shop documents and other unstructured data.
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Collaborate with data engineers and application teams to integrate LLM/GenAI services into digital inspection applications, portals and shop‑floor tools.
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Work closely with domain experts to understand workflows and pain points, translate them into AI use cases, and drive Po Cs and pilots to production.
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Ensure responsible and compliant use of LLM/GenAI, including appropriate guardrails, grounding, and documentation to support safety, quality and governance requirements.
Ideal Candidate:
- The ideal candidate is a hands-on data scientist with deep expertise in NLP, LLMs and GenAI who can design, build and productionize RAG-based, document-centric solutions. They are equally comfortable discussing model architectures and evaluation as they are partnering with inspection engineers and shop operations to translate real-world pain points into impactful AI products.
Required Qualifications
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Master’s or PhD in Computer Science, Electrical/Computer Engineering, Artificial Intelligence, Applied Mathematics, Statistics, or a related quantitative field.
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Candidates with a Bachelor’s degree in a relevant discipline and strong, proven industry experience in computer vision, deep learning or industrial inspection are also encouraged to apply.
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Additional coursework, certifications or research experience in natural language processing, information retrieval, machine learning or software engineering will be considered a plus.
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5+ years of hands‑on data science, NLP or AI application development. Solid understanding of NLP/LLM & GenAI techniques, such as text classification, information extraction, question answering, summarization, prompt engineering, RAG and evaluation of language models.
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Strong programming skills in Python, with experience using relevant libraries and frameworks (e.g. Py Torch / Tensor Flow, Hugging Face, Lang Chain/Llama Index, spa Cy, FastAPI).
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Familiarity with cloud and data platforms (e.g. Databricks, AWS, Azure, vector databases, search engines) is a plus.
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Strong cross‑functional collaboration, problem‑solving, and influencing skills. Comfortable working in an ambiguous, fast‑evolving environment.
Preferred Qualifications
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Quick learner, strategically prioritizes work, committed
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Strong communicator, decision-maker, collaborative
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analytical-minded, challenges existing processes, critical thinker
At GE Aerospace, we have a relentless dedication to the future of safe and more sustainable flight and believe in our talented people to make it happen. Here, you will have the opportunity to work on really cool things with really smart and collaborative people. Together, we will mobilize a new era of growth in aerospace and defense. Where others stop, we accelerate.
Additional Information Relocation Assistance Provided: No
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About General Electric
Reviews
3.5
5 reviews
Work Life Balance
3.8
Compensation
3.2
Culture
3.1
Career
3.4
Management
2.8
55%
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Pros
Good career development programs (FMP, CAS)
Above-average salary and compensation
Better work-life balance
Cons
Concerns about long-term company viability
Competitive up-or-out culture in programs
Uncertainty about software development management
Salary Ranges
24 data points
Mid/L4
Senior/L5
Mid/L4 · Lead Scientist - Data & Knowledge Platforms
1 reports
$184,080
total / year
Base
$141,600
Stock
-
Bonus
-
$184,080
$184,080
Interview Experience
5 interviews
Difficulty
2.8
/ 5
Duration
14-28 weeks
Offer Rate
20%
Experience
Positive 20%
Neutral 40%
Negative 40%
Interview Process
1
Application Review
2
Recruiter/HR Screen
3
Hiring Manager Interview
4
Final Interview Round
5
Offer Decision
Common Questions
Behavioral/STAR
Technical Knowledge
Past Experience
Culture Fit
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