Simulation Engineer - Industrial AI Solutions
Simulation Engineer - Industrial AI Solutions
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Job Description
Roles & Responsibilities :
Model Development & Validation:
Develop train and validate machine learning models (regression classification time series etc.) based on project requirements and defined hypotheses.
Build and calibrate simulation models of industrial processes equipment and systems.
Utilize data analytics techniques to extract insights from large datasets and identify opportunities for optimization.
Integrate domain knowledge and physics-based principles into data-driven models to improve accuracy and robustness.
Conduct statistical analysis develop visualizations and communicate with stakeholders.
Develop and maintain documentation of models algorithms and code.
Stay up-to-date on the latest advances in AI ML and simulation techniques.
Explore new technologies and approaches to solve industrial problems.
ML Engineering & MLOps:
Implement MLOps best practices for model deployment monitoring and maintenance.
Automate model training validation and deployment pipelines.
Work with IT and DevOps teams to deploy models into production environments.
Monitor model performance and retrain models as needed to maintain accuracy.
Edge Analytics & Deployment:
Develop and deploy lightweight AI models for edge computing environments.
Design and implement data pipelines for edge data collection and processing.
Work with hardware engineers to integrate models with edge systems.
Collaboration & Communication:
Collaborate with domain experts program managers and other data analysts / engineers to understand business problems and develop solutions.
Communicate complex technical concepts to non-technical audiences.
Participate in code reviews and knowledge sharing sessions.
Contribute to the development of best practices and standards for data science and AI.
Qualifications :
Educational qualification:
Masters degree in Computer Science Data Science Engineering (Chemical Mechanical Electrical Industrial) Statistics or a related quantitative field
Experience :
5-8 years of experience in data science machine learning or simulation modeling with a track record of applying these techniques to solve problems in industrial domain.
Experience with developing and deploying AI models in production environments.
Experience with edge computing and deploying models on edge devices is a plus.
Mandatory/requires Skills :
Strong programming skills in Python R or other relevant languages.
Proficiency with machine learning libraries (e.g. TensorFlow PyTorch scikit-learn).
Experience with data analytics platforms and tools (e.g. Tableau Power BI SQL NoSQL).
Familiarity with cloud computing platforms (e.g. AWS Azure GCP).
Understanding of MLOps principles and tools (e.g. Kubeflow MLflow).
Understanding of industrial operations within at least one of the following industries is mandatory: Manufacturing Steel Oil & Gas Energy Mining Chemicals Textile Pharma.
Ability to translate domain expertise into data-driven models and simulation scenarios.
Preferred Skills :
Excellent communication problem-solving and analytical skills.
Ability to work independently and as part of a team.
Strong organizational and time management skills.
Additional Information :
The Simulation Engineer or Data Scientist will be responsible for developing and deploying advanced Industrial AI and Process Twin solutions. This role requires a strong foundation in data science statistical modeling machine learning and simulation techniques coupled with a solid understanding of industrial processes. The ideal candidate will work closely with domain experts and program managers to build validate and deploy AI models conduct simulation studies and develop edge-based analytics solutions to drive performance improvements and optimization for our clients. You will also be implementing MLOps best practices to ensure models are reliable and scalable in production environments
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
