Senior Software Engineer-Research Engineer (Aerospace)

Skills

Senior Research Engineer

We are looking for a senior research engineer with an interest in growing career towards research and development activities in the areas of AI / ML/ LLM etc.. You will be responsible for technically supporting the conceptualisation, development and implementation of digital technologies with focus on improving the aerospace design and manufacturing processes. You will be part of the team to shape and manage research and development projects in the TRL1-6 range, using your diverse skills in latest digital technologies.

General:

• Must possess excellent Communication and presentation skills to communicate with business stakeholders
• Must possess experience with cloud platforms (AWS, Azure, GCP) and knowledge of infrastructure requirements for hosting and deploying LLMs, including handling terabyte-scale datasets.
• Must possess strong understanding of data pre-processing, annotation, and management specific to the aerospace sector, ensuring data quality and compliance with industry regulations.
• Ability to work closely with aerospace and defence teams to identify pain points and develop custom AI-driven solutions to optimize processes, reduce costs, and improve operational efficiency.
• Must possess deep understanding of aerospace and defence industry workflows, standards, and requirements, with hands-on experience in applying AI/ML in areas like design, manufacturing, maintenance, or supply chain.

Essential:

• Bachelors in Engineering or higher, with minimum of 10 years of relevant experience in machine learning, at least 6 years of experience in building and deploying machine learning models and systems & 3+ years of hands-on experience with LLMs and Generative AI.
• Design data pipelines to handle large-scale data for training and fine-tuning LLMs, ensuring data security and compliance with aerospace and defence standards.
• Fine-tune and adapt pre-trained LLMs to specific use cases such as design automation, technical documentation, intelligent fault diagnosis, and predictive analytics in aerospace and defence manufacturing.
• Implement best practices for deploying LLMs on cloud, hybrid, or on premise infrastructure, ensuring scalability, reliability, and cost-effectiveness.
• Experience with building RAG systems and Prompt engineering.
• Proficiency in programming with Python, Lang chain, and SQL.
• Excellent understanding of machine learning techniques and algorithms, such as GPTs, CNN, RNN, k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Strong understanding of various machine learning models, especially how generative models like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Transformers are deployed in large-scaled infrastructure, cloud or in-premise
• Ability to conduct POCs and guide team members to extract valuable insights and drive data-driven decision-making.
• Evaluate and select appropriate AI / Generative AI tools and machine learning models for tasks.
• Strong software development skills, including version control (e.g., Git), debugging, testing, and documentation. Familiarity with containerization (e.g., Docker) and orchestration (e.g., Kubernetes) is beneficial.
• Stay updated with latest developments in NLP, ML, AI, LLM technology around the globe relevant to aerospace and defence sector and work along with team to quickly leverage, test and validate new solutions applicable to the working projects by applying cutting edge technologies.
• Should have strong problem-solving skills and ability to collaborate with cross-functional teams, including domain experts, data scientists, and engineering teams, to gather requirements and translate them into scalable AI / ML/ LLM applications.

Desirable:

• Shall contribute to the development of GKN as a 'Great Place to Work', aligned with the core principles - safe, innovative, open & honest, care & respect, and ownership

Personal Attributes:

• Be capable of:
  • Working in research projects
  • Focus on continuous improvement and problem solving
  • Commit to relationship building with customers and colleagues
  • Work effectively with diverse groups of people
  
  

• Consistently demonstrate the following attributes and values:
  • Recognise what people need from you and respond flexibly
  • Demonstrates interpersonal sensitivity
  • Builds confidence in others
  • Hunger for continuous learning, growth and curiosity with an innovative mind set
  • Stretch out of comfort zone
  • Challenge assumptions and hearsay, and look for evidence
  • Trusts people to do their job
• Personal knowledge:
  • Knows what drives profit within their business area
  • Knows the key stakeholders

In depth knowledge of own industry and customers

General:

• Must possess excellent Communication and presentation skills to communicate with business stakeholders
• Must possess experience with cloud platforms (AWS, Azure, GCP) and knowledge of infrastructure requirements for hosting and deploying LLMs, including handling terabyte-scale datasets.
• Must possess strong understanding of data pre-processing, annotation, and management specific to the aerospace sector, ensuring data quality and compliance with industry regulations.
• Ability to work closely with aerospace and defence teams to identify pain points and develop custom AI-driven solutions to optimize processes, reduce costs, and improve operational efficiency.
• Must possess deep understanding of aerospace and defence industry workflows, standards, and requirements, with hands-on experience in applying AI/ML in areas like design, manufacturing, maintenance, or supply chain.

Essential:

• Bachelors in Engineering or higher, with minimum of 10 years of relevant experience in machine learning, at least 6 years of experience in building and deploying machine learning models and systems & 3+ years of hands-on experience with LLMs and Generative AI.
• Design data pipelines to handle large-scale data for training and fine-tuning LLMs, ensuring data security and compliance with aerospace and defence standards.
• Fine-tune and adapt pre-trained LLMs to specific use cases such as design automation, technical documentation, intelligent fault diagnosis, and predictive analytics in aerospace and defence manufacturing.
• Implement best practices for deploying LLMs on cloud, hybrid, or on premise infrastructure, ensuring scalability, reliability, and cost-effectiveness.
• Experience with building RAG systems and Prompt engineering.
• Proficiency in programming with Python, Lang chain, and SQL.
• Excellent understanding of machine learning techniques and algorithms, such as GPTs, CNN, RNN, k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Strong understanding of various machine learning models, especially how generative models like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Transformers are deployed in large-scaled infrastructure, cloud or in-premise
• Ability to conduct POCs and guide team members to extract valuable insights and drive data-driven decision-making.
• Evaluate and select appropriate AI / Generative AI tools and machine learning models for tasks.
• Strong software development skills, including version control (e.g., Git), debugging, testing, and documentation. Familiarity with containerization (e.g., Docker) and orchestration (e.g., Kubernetes) is beneficial.
• Stay updated with latest developments in NLP, ML, AI, LLM technology around the globe relevant to aerospace and defence sector and work along with team to quickly leverage, test and validate new solutions applicable to the working projects by applying cutting edge technologies.
• Should have strong problem-solving skills and ability to collaborate with cross-functional teams, including domain experts, data scientists, and engineering teams, to gather requirements and translate them into scalable AI / ML/ LLM applications.

Desirable:

• Shall contribute to the development of GKN as a 'Great Place to Work', aligned with the core principles - safe, innovative, open & honest, care & respect, and ownership

Personal Attributes:

• Be capable of:
  • Working in research projects
  • Focus on continuous improvement and problem solving
  • Commit to relationship building with customers and colleagues
  • Work effectively with diverse groups of people
  
  

• Consistently demonstrate the following attributes and values:
  • Recognise what people need from you and respond flexibly
  • Demonstrates interpersonal sensitivity
  • Builds confidence in others
  • Hunger for continuous learning, growth and curiosity with an innovative mind set
  • Stretch out of comfort zone
  • Challenge assumptions and hearsay, and look for evidence
  • Trusts people to do their job
• Personal knowledge:
  • Knows what drives profit within their business area
  • Knows the key stakeholders

In depth knowledge of own industry and customers