AI Software Engineer – ML, Data Pipelines & Deployment (Hybrid)

Safran Engineering Services UK Limited; Gloucester; Híbrido; GBP 65.000 - 85.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Early finish Friday (Friday midday)

5 weeks holiday per year + bankhols

Personal pension 5% employee / 8% from

Death in service life insurance 4x

Health insurance (Bupa)

PEGI Share Scheme

Career development within Safran Group

Training & development focus

Onsite canteen

Descripción de la vacante

Safran Engineering Services UK Limited is recruiting an AI Software Engineer to develop, test and deploy AI solutions for major programmes within the Engineering Methods & Tools Department in the UK.

The role involves hands-on development, data pipeline management and cloud deployment, working with AI software engineers, data scientists and stakeholders. Initial onsite work, with potential for hybrid work up to two days per week after early priorities are set.

Formación

• Hands-on development of AI/ML solutions for engineering applications.
• Strong software engineering practices including deployment and maintenance of AI systems.
• Experience with data pipelines and data quality.
• Proficient in Python and Java and able to work with cloud-based tools.

Responsabilidades

• Prototype, design and test AI and ML solutions for engineering applications.
• Build maintainable and scalable ML systems and AI solutions.
• Manage data pipelines and ensure data quality.
• Support cloud deployment and transition into operation.

Conocimientos

Python

Java

LLMs

Machine Learning

NLP

Computer Vision

Data Analysis

Communication

Herramientas

AWS

Databases

Data pipelines

Descripción del empleo

Safran Engineering Services UK Limited is recruiting an AI Software Engineer to develop, test and deploy AI solutions for major programmes within the Engineering Methods & Tools Department in the UK.

The role involves hands-on development, data pipeline management and cloud deployment, working with AI software engineers, data scientists and stakeholders. Initial onsite work, with potential for hybrid work up to two days per week after early priorities are set.