CAD AI Software Engineer Big Data Pipelines

Do you feel like you’re just coding toy problems without making a real impact? Join us for a fulfilling career as a Software Engineer specializing in Big Data Pipelines and ML Automation , where you’ll tackle concretely defined, real-world challenges. At Imnoo, you’ll make a significant impact on automating manufacturing processes  through robust big data engineering , scalable data pipelines , and machine learning integrations , gaining invaluable experience in Python development , C# programming , C++ expertise , and deep satisfaction along the way We’re looking for an experienced Software Engineer - Big Data / Pipelines with an ML Flavour  who is passionate about automation , AI/ML engineering , and ready to build scalable ETL pipelines , process massive datasets, and infuse machine learning models  into production systems using strong Python coding skills , C# development , and C++ performance optimization . Why Join Us? | Career in Big Data Engineering & ML A clear path for career growth  in machine learning engineering  and data engineering roles . The opportunity to solve real and meaningful challenges in data-intensive manufacturing automation , computer vision , and AI-driven pipelines . A dynamic and flexible work environment supporting remote software development . Opportunities for professional development , certification support  in AWS, Azure, and MLOps . A platform to share your ideas and opinions, where they are highly valued in our startup tech team . Your Playground | Hands-On Big Data & ML Projects Work on real/hands-on problems involving big data processing and automation tools  for industrial AI . Develop efficient data pipelines  and tools that directly impact the day-to-day operations of our manufacturers Design and implement advanced data extraction , feature engineering , processing, and pipeline orchestration  solutions for handling CAD, 2D, 3D, and large-scale batch data (filtered/unfiltered) with a focus on ML applications  like deep learning models  and predictive analytics . Engineer scalable big data infrastructure , cloud-native services , RESTful APIs , automated ML model training and deployment , and CI/CD build tools  using Docker  and Kubernetes . Own services end-to-end, from proof of concept to production-ready solutions in high-load environments with scalability testing  and performance tuning . Gain in-depth knowledge of interconnected cloud computing , big data ecosystems  (Hadoop, Spark), computer vision pipelines , and ML frameworks  across Python , C# , C++ , and multiple platforms like Linux/Unix systems . Maintain and enhance optimization algorithms , machine learning services , and neural network integrations  within data pipelines. Improve 2D/3D/CAD tools and solutions through automated, data-driven workflows , including geometric modeling , simulation tools , and GPU acceleration  with CUDA.SEO optimization:Big Data Engineer ,  Data Pipeline Developer ,  Machine Learning Engineer ,  Python Developer ,  C# Developer ,  C++ Software Engineer ,  Cloud Services Engineer ,  AWS Developer ,  Azure Developer ,  Automation Engineer ,  Distributed Systems Developer ,  MLOps Specialist ,  CAD Data Processing ,  3D Geometry Software Engineer ,  ETL Pipeline Developer ,  Industrial Automation Software Engineer ,  IoT Data Engineer ,  High-Performance Computing ,  Data Pipeline Architect ,  Software Engineer for Big Data and ML ,  Cloud-Native Application Developer ,  Data Processing Optimization ,  Parallel Computing Engineer . (Middlemen such as recruiting agencies are not welcome and will be automatically disqualified) Best to Have: | Essential Skills for Big Data ML Engineer Roles Strong software / coding skills in Python development , C# .NET programming , and C++ expertise  with a passion for machine learning , deep learning , and process automation . 5+ years of experience in dynamically typed (e.g., Python scripting ) and statically typed languages (e.g., C# backend , C++ systems programming ). CAD system development/work experience Strong problem-solving skills for building efficient, scalable data pipelines  and ML workflows  under production constraints. Foundations or experience in 3D/geometry processing, game development engines  (Unity/Unreal), fluid simulations, real-time rendering , CUDA GPU programming , or similar technologies to handle complex big data analytics  and spatial data . Nice to Have: | Preferred Qualifications for ML Pipeline Developers Educational background in mathematics, statistics, or computer science  with strong dedication and experience in applied technologies like applied ML  and data science  (nice to have). CAD data processing  experience, including STEP/IGES formats (nice to have). Industry/Mechanical experience in CNC machining , robotics automation , and related fields (optional). Hands-on experience in Frontend development  (e.g., Angular, React) and Backend engineering  (Node.js, .NET) (optional). Full-stack development  experience with microservices architecture  (optional). Familiarity with popular machine learning libraries  and deep learning frameworks , such as scikit-learn, PyTorch , TensorFlow , and PyTorch Lightning  (nice to have). Experience with ML model industrialization  tools, including model quantization , ONNX export , Docker containerization , and serverless deployment  (nice to have). Knowledge of MLOps practices , ML pipeline development , and tools like MLflow or Kubeflow (nice to have). Expertise in big data processing , data clustering , anomaly detection , filtering, indexing, and querying large datasets efficiently using Elasticsearch  or BigQuery  (nice to have). Proficiency in automated model training pipelines  and A/B testing deployment  (optional). Strategic data analysis  and research skills, including statistical modeling , error propagation analysis , and identifying clusters or outliers in high-dimensional data  (optional). Experience with cloud platforms , particularly AWS services  (S3, Lambda, SageMaker) and Azure Cloud Services  (Data Factory, ML Studio) (optional). Strong database skills : SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) for data warehousing  (optional). Deep understanding of advanced data structures  and algorithms for efficient querying  (optional).