Artificial Intelligence (AI) for engineering workflow and modeling, Machine Learning, Deep Learning, FEA tools, ANSYS, numerical methods, virtual structure modeling, Scientific Computing, computational geometry, Rendering, ABAQUS, DOE analysis, thermo-mechanical modeling, Victory Process, SPX, SEM3D

Performance Testing, Servicenow, Gcp, Incident Management, Cloud Infrastructure, Itil, Azure, AWS, Security and Compliance Testing, Major Incident Notification Tooling, AI-driven Quality Engineering Tools

Data Privacy, Containers, Python, Access Control, AWS Bedrock, Lang Chain, Lang Graph, Auditability, Lang Smith, Graph technologies, AI Eval tools, Semantic Kernel, Infrastructure as code, Model risk, Llama Index, CI CD pipelines, Serverless deployment, Responsible AI, AI governance

Machine Learning, Pyspark, Pytorch, Docker, Python, AWS, Azure DevOps, Sklearn, Automated Testing, Git, MLops, Databricks, Kubernetes, MLflow, GitHub Actions, Experiment tracking, Observability, Monitoring, Model registries, Model drift analysis, Governance, versioning, Lakehouse, CI/CD, Evidently AI

cloud platform , Machine Learning, AI ML, Python, Deep Learning

Algorithms, Software Development, Machine Learning, Logging, data structures, Python, multi-agent frameworks, tracing, Go, Monitoring, AI ML technologies, simulation orchestration environments

Selenium, Jira, Sql, Gen AI tools, DevOps practices, cloud platforms AWS