Research Engineer/Specialist - Digital Engineering & Simulation

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.

Develop, execute, implement and check methods, plans, toolsets and approaches that are appropriate and compliant to achieve digital solutions for the process intended
Identify technical problems and apply/integrate solutions as needed with a set based design approach
Analyse customer requirements and define technical solutions as input for proposals to develop proof of concepts
Work with cross functional teams and multiple sites during the development process
Generate assigned project deliverables, documents, and reports according to the project milestones
Support and participate in technical reviews, including the creation and preparation of technical data and presentations as needed and support other engineers with peer to peer reviews
Support Lean culture and improvement initiatives in the organisation
Take part in regular sprint planning meetings to plan, review and deliver outputs based on agile philosophy
Support in creation of training material and knowledge sharing in the relevant area of work
Support idea generation and CI activities
Perform all activities independently and help other engineers within the program as required

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,