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The position to be filled adwäisEO is looking for an experienced AI & Data Technical lead to define and drive the technical strategy of its data team in support of innovative Earth Observation (EO) data valorization, and to carry out projects as project manager, expert, developer or operator in a small team of 3 people to be embedded in a larger team made of the grouping of sister companies' teams. The role is centered around the survey, design, development, optimization and deployment of state-of-the-art machine learning/deep learning algorithms as well as the training & deployment of generative AI models (geo-VLM) to enhance current workflow on EO imagery processing & analysis. The candidate will work on the development of deep learning and AI processes to support the generation of innovative new data derived information. The activities will include the development of advanced models and pipelines such as foundation model for EO data, Vision transformers and attention-based architecture for multimodal dataset and scalable workflows leveraging High-Performance Computing (HPC). She/he will be contributing to: • Survey of and feedback on technological progress in the deep learning and machine learning technology evolution in the domain, • Design and development of deep learning algorithms for EO imagery and related datasets • Development of related documentation (Algorithm baseline, functional analysis) • Definition of data requirement for processing chain. • Writing engineering documentation and technical reports in English. • Participation to the Bids and proposals. Must Have Requirements Profile • 5-8 years of experience in AI and data science algorithm development (preferable for satellite Remote Sensing or imagery/video data). Technical Skills (Hard Skills) • Experience with Deep Learning and/or simple Machine Learning techniques, attention- based architecture, foundation model (adapting, fine-tuning, evaluating and, where relevant, pre-training foundation models for Earth Observation data -Vision and Large Language models in particular); • Proficiency in python with solid object-oriented programming practices and experience building modular, scalable pipelines; • Strong hands-on proficiency in PyTorch; (familiarity with TensorFlow or other ML frameworks is an advantage); • Hands-on experience with popular specialized libraries (rasterio, xarray, geopandas, terratorch); • Familiarity with Linux environments; • Knowledge of Rest API for querying EO data repositories (e.g. STAC), with expertise in creating, validating, and maintaining robust training datasets; • Demonstrated experience deploying machine learning or deep learning models in production environments; • Hands-on experience with Docker and containerized AI applications; • Experience designing, developing and maintaining model inference APIs or services; • Good understanding of the end-to-end machine learning lifecycle, including data preparation, training, validation, deployment, monitoring, versioning and retraining; • Hands-on experience with GPU-based training and/or inference, HPC/Slurm; • Practical experience with at least one production computing environment, such as cloud platforms, Kubernetes-based infrastructures or HPC systems. Behavior skills (Soft Skills) • Analytical, critical and proactive mind; • Very good task prioritization and organization: ability to allocate tasks and track progress; • Very good technical mentoring (supporting and upskilling junior profiles); • Very good communication skills: acting as the link between the technical team and management/other departments; • Fluency in English (spoken and written) is essential; • High degree of autonomy and decision-making; • Versatility: comfortable operating at a hands-on capacity while coordinating the team's work. Being considered as a plus: • Knowledge of other programming or coding languages such as IDL, C, C++; • Experience with physical modelling (e.g. hydraulic, hydrology, crop, …); • MLOps Skills (Docker, microservices); • Experience with HPC, distributed training, or GPU-accelerated computation; • Knowledge of Satellite data processing; • PostDoc in Remote Sensing or ML. What we offer ❖ A professional, stimulating and human working environment in IT & Earth Observation Data sector based on proximity management, ❖ A real possibility of evolution depending on your involvement in your work, ❖ Lunch vouchers, ❖ Supplementary Health insurance, ❖ Bonuses, ❖ Canteen, ❖ Pleasant premises in the heart of the Luxembourg countryside, ❖ Free car park Why Join Us? ❖ Be part of innovative projects with strong impact in high-tech and strategic sectors. ❖ Work in an agile, ambitious environment at the heart of a fast-growing industry. ❖ Contribute to the development of cutting-edge solutions and help shape the future of our business. ❖ Join a dynamic and passionate team, committed to excellence and continuous improvement
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.