Roles & responsibilities

The role of an Architect in the field of General Artificial Intelligence (GenAI) involves designing and developing intelligent systems that can mimic human intelligence and perform complex tasks. Here are some of the key responsibilities of an Architect in GenAI: 

1. Research and Development: Architects in GenAI are responsible for conducting extensive research to understand the latest advancements in artificial intelligence, machine learning, and cognitive computing. They stay updated with emerging technologies and techniques to incorporate them into their designs.

 2. System Design: Architects design the overall structure and architecture of GenAI systems. They define the components, modules, and algorithms required to achieve the desired level of intelligence. They also consider factors like scalability, performance, and integration with existing systems.

 3. Algorithm Development: Architects develop and optimize algorithms that enable GenAI systems to learn, reason, and make decisions. They work on various techniques such as deep learning, reinforcement learning, natural language processing, and computer vision to enhance the intelligence of the system.

 4. Data Modeling: Architects design data models and schemas to represent and

organize the data required for training and inference in GenAI systems. They ensure the data is clean, relevant, and properly structured to improve the accuracy and efficiency of the system.

 5. Collaboration: Architects collaborate with cross-functional teams including data scientists, engineers, and domain experts to understand the requirements and constraints of the GenAI system. They work closely with these teams to ensure the successful implementation and deployment of the system.

6. Performance Optimization: Architects optimize the performance of GenAI systems by fine-tuning algorithms, improving computational efficiency, and reducing latency. They analyze and address bottlenecks to enhance the overall system performance.

7. Ethical Considerations: Architects in GenAI are responsible for considering ethical implications and potential biases in the design and implementation of intelligent systems. They ensure fairness, transparency, and accountability in the decision-making processes of the GenAI system. 

8. Documentation and Communication: Architects document the design decisions, methodologies, and technical specifications of the GenAI system. They also communicate complex concepts and ideas to stakeholders, including management, clients, and end-users. 

Overall, the role of an Architect in GenAI requires a deep understanding of artificial intelligence, machine learning, and cognitive computing, along with strong analytical, problem-solving, and communication skills.

Roles & responsibilities

The role of an Architect in the field of General Artificial Intelligence (GenAI) involves designing and developing intelligent systems that can mimic human intelligence and perform complex tasks. Here are some of the key responsibilities of an Architect in GenAI: 

1. Research and Development: Architects in GenAI are responsible for conducting extensive research to understand the latest advancements in artificial intelligence, machine learning, and cognitive computing. They stay updated with emerging technologies and techniques to incorporate them into their designs.

 2. System Design: Architects design the overall structure and architecture of GenAI systems. They define the components, modules, and algorithms required to achieve the desired level of intelligence. They also consider factors like scalability, performance, and integration with existing systems.

 3. Algorithm Development: Architects develop and optimize algorithms that enable GenAI systems to learn, reason, and make decisions. They work on various techniques such as deep learning, reinforcement learning, natural language processing, and computer vision to enhance the intelligence of the system.

 4. Data Modeling: Architects design data models and schemas to represent and

organize the data required for training and inference in GenAI systems. They ensure the data is clean, relevant, and properly structured to improve the accuracy and efficiency of the system.

 5. Collaboration: Architects collaborate with cross-functional teams including data scientists, engineers, and domain experts to understand the requirements and constraints of the GenAI system. They work closely with these teams to ensure the successful implementation and deployment of the system.

6. Performance Optimization: Architects optimize the performance of GenAI systems by fine-tuning algorithms, improving computational efficiency, and reducing latency. They analyze and address bottlenecks to enhance the overall system performance.

7. Ethical Considerations: Architects in GenAI are responsible for considering ethical implications and potential biases in the design and implementation of intelligent systems. They ensure fairness, transparency, and accountability in the decision-making processes of the GenAI system. 

8. Documentation and Communication: Architects document the design decisions, methodologies, and technical specifications of the GenAI system. They also communicate complex concepts and ideas to stakeholders, including management, clients, and end-users. 

Overall, the role of an Architect in GenAI requires a deep understanding of artificial intelligence, machine learning, and cognitive computing, along with strong analytical, problem-solving, and communication skills.

This role is for you if you have  the below

Educational qualifications

-Bachelor's degree in Computer Science

Work experience

12+ Years of Experience

Mandatory  technical & functional skills

—Develop Technical & Solution architecture for Generative AI Use cases across product teams, with strong foundation on Micro Services Architecture , based on various cloud platforms, Azure (ML studio), /GCP ( Vertex AI) /AWS ( sagemaker) —Proficiency in Python, Java, or C++, and machine learning frameworks like TensorFlow or PyTorch is crucial. —Good understanding of latest Technological Advancements in Generative AI, predominately in Large Language Models and  Large Vision Models, and Frameworks (LangChain etc) and Vector DBs, such as FAISS, ChromaDB,

Good knowledge on End-2-End model building pipeline and underlying orchestration platforms such as Kubernetes, and deployment  and tracking, MLOps.

Preferred technical & functional skills

—In depth understanding of building of Large Language Models and fine / domain tuning of LLMs  and Evaluation Techniques —Strong oral and written communication skills with the ability to communicate technical and non-technical concepts to peers and stakeholders —Ability to work independently with minimal supervision, and escalate when needed.

Key behavioral attributes/requirements

—Ability to mentor junior developers —Ability to own project deliverables, not just individual tasks —Understand business objectives and functions to support data needs.