Big data engineer Jobs in Winterthur

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).