AI Expert

To drive innovation in heavy engineering applications in the area of stress analysis using AI/ML and data analytics. This role leverages expertise in AI, ML, and Gen AI to optimize design, simulation, and predictive analytics processes. Proficiency in FEA tools such as ANSYS, ABAQUS, Optistruct, and Hypermesh is essential, with responsibilities focused on streamlining engineering workflows and advancing product development.

Key Responsibilities:

Develop AI/ML and Gen AI Models: Design, train, and implement machine learning and Gen AI models to drive efficiencies in structural analysis, and simulation processes.

Integrate Gen AI for Automation: Leverage Generative AI to automate complex engineering workflows, optimize design iterations, and enhance predictive accuracy in simulations.

FEA Validation and Analysis: Use FEA tools (ANSYS, Hypermesh) to validate AI/ML and Gen AI predictions, ensuring robust and reliable structural performance assessments.

Data Analytics and Pipeline Development: Build data pipelines to process large engineering datasets and extract actionable insights, enabling the improvement and validation of AI/ML models.

Cross-Functional Collaboration: Work closely with engineering, R&D, and data science teams to integrate AI/ML and Gen AI solutions into existing engineering workflows.

Documentation and Reporting: Prepare comprehensive reports and documentation on AI/ML model performance, methodology, and implementation outcomes for stakeholder review.

Stay Updated with Industry Trends: Keep abreast of advancements in AI, ML, and Gen AI relevant to engineering, introducing new tools, frameworks, and methodologies to the team.

Qualifications:

Education: Bachelor’s or Master’s degree in Mechanical Engineering.

Experience: 5+ years in stress analysis of heavy engineering with working knowledge on AI/ML, Gen AI, data analytics

Technical Skills:

Proficiency in FEA tools (ANSYS ABAQUS, Optistruct, Hypermesh) with a focus on structural and predictive analysis.

Strong programming skills in Python, R, or MATLAB, with experience in building AI/ML models and working with Gen AI frameworks.

Experience with machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and Generative AI platforms (e.g., OpenAI, Hugging Face).

Strong skills in data preprocessing, data analytics, and statistical techniques for engineering applications.

Soft Skills: Excellent problem-solving and analytical skills, with the ability to communicate complex AI/ML and engineering concepts effectively to diverse stakeholders.

Preferred Skills:

Experience with deploying ML and Gen AI models in production environments.

Knowledge of cloud-based AI/ML and Gen AI services (e.g., AWS, Azure, Google Cloud).

Familiarity with data visualization tools (e.g., Tableau, Power BI) and libraries (e.g., Matplotlib, Seaborn).

Background in heavy engineering, with a solid understanding of  stress analysis and design principles.