Lead Artificial Intelligence (AI) Application Architect
Hyderābād (Hyderābād) IT development
Job description
Job Description Summary
Artificial Intelligence (AI) Application architect is experienced in building grid innovation applications using model-to-code, and architecting prototype systems to validate and verify in containerized form. The AI application architect works in close collaboration with GA’s Innovation, PM/ R&D and regional teams in demonstration of innovation Apps in virtualized systems.This role will also be responsible for building the data analytics applications framework and work closely with other functions across Grid Automation (GA) business to identify areas where the business can leverage data and data analytics to drive efficiency, increase customer satisfaction, and develop POCs to solve critical problems for our customers.
As part of the MLOps architecture development, the AI application architect will enable the ML Model end to end lifecycle i. e., from Commissioning training datasets to deploying it in production environment through automated CI/CD pipeline.
Job Description
The AI application architect is responsible for
Demonstrate novel & transformational applications/analytics to drive innovation & differentiation.
Define the framework to collect, structure and use of databases for AI, to extract value.
Develop AI/ML application to build differentiated products and solutions; with ability to work on customers value-driven applications/analytics to drive innovations.
Design and deploy high-quality, scalable, and secure AI/ML models and applications on the GE GridNode/ edge platforms, using container or microservices principles. Develop and implement strategies for optimizing the performance and scalability of machine learning models in production.
Collaborate with product management, R&D, and other functions in to understand their needs and develop innovative solutions. Implement and maintain data pipelines for AI/ML models. Monitor and optimize the performance of AI/ML models in production.
Identification of Intellectual property/IP clearance.
Collaborate with cross-functional teams.
Qualification/Requirements:
Master’s/PhD Degree in computer science, Information technology (IT), electrical engineering, or electric power engineering, specifically in the computer and electric power engineering field with minimum 6+ years of data science working experience.
6+ years of professional working environment and knowledge of artificial intelligence (AI) and machine learning (ML), including, unsupervised learning, supervised learning, and reinforcement learning, large language models (LLMs).
5-10 years R&D or Applications experience related to power system protection and automation.
Proven experience in applying AI/ML frameworks/workflows, AI/MLOps with CI/CD using Cloud-native and on-prem development and deployment in OT/industrial automation environments.
Hands-on professional experience in developing and testing AI/ML algorithms; AND/OR demonstrated professional experience with different scenarios of grid/physics models in power system simulation tools, MATLAB/PSCAD; as well as dynamics PSS/E, Digsilent, and equivalent.
Experience with MLOps principles.
Experience with DevOps, data pipelines, Azure ML registry, deployment methods viz. Docker, K8s, etc.
Able to share ideas and work well in a team environment, proactive approach to tasks displaying initiative.
Flexible and adaptable; open to change and modification of tasks, working in multi-tasking environment.
Demonstrated professional experience with different scenarios of appropriate AI/ML models for energy/grid applications.
Desired Characteristics:
6+ years of research or industry experience with simulation using scientific programming tools or languages, such as MATLAB, C++, C#, or Python, R, etc.
3 years of experience in developing and implementing ML models, such as predictive maintenance, load forecasting, and grid optimization using cloud servers such as AWS Sagemaker or equivalent in the Power Systems domain.
2 years of experience in a MLOps, data engineering, and cloud, working with real-time distribution grid data.
Experience with Linux virtualized system deployment using VM, Hypervisor (EsXi, KVM, Xen etc.), Dockers and related tools.
Experience as a system architect, team lead, industry recognized subject matter expert.
Advanced experience in utilizing and applying common programming languages, such as Python, C/C++, Java, Spark and Hadoop, R Programming, Kafka, C#, MATLAB, along with good familiarity with power system modelling and data communication format.
Expertise in Machine learning/Deep learning methods - LLM, NLP, Computer vision/ Image Processing.
Expertise of GraphDB, SQL/NoSQL, MS Access, databases.
Understanding/experience applying data analytics for Electrical Power System or industrial OT system.
Understanding of GPU Experience, Spark, Scala for distributed computing.
Strong root causing, trouble shooting and debugging skills using tools such as Wireshark, TCPDump and other Linux and Windows system tools.
Strong communication skills and a proactive and open approach to conflict resolution.
Strong organizational skills, self-motivated, and self-directed.
Knowledge of modern protection and control and distribution automation developments and trends.
Proven record of writing and presenting papers at industry conferences/journals.
Additional Information
Relocation Assistance Provided: Yes