Responsibilities:

Agent Development: Design and develop intelligent agents that can interact with users, understand complex tasks, and generate human like responses using LLMs and other AI techniques.

Custom LLM development: Finetune and customize existing LLMs or build new models from scratch to address specific business needs and domains.

Framework Expertise: Utilize and contribute to open-source or internal frameworks for LLM development, deployment, and management.

Application development: Build applications that leverage generative AI capabilities, including chatbots, text generation tools, code generation tools and more.

Deployment and Infrastructure: Deploy and manage AI services on AWS, ensuring scalability, reliability, and security.

Collaboration:   Work closely with cross functional teams, including data scientists, software engineers, and product managers, to understand requirement and develop successful AI solutions.

Research and Development: Stay updated on latest advancements in generative AI and explore new technologies and techniques to improve the performance and capabilities of our AI systems.

Requirements:

Experience: 3-5 years of experience in developing and deploying AI solutions with a focus on generative AI and LLMs

LLMs:    Strong understanding of LLM architectures, training models, and applications. Experience with popular LLMs such as GPT-4, Llama-2, Llama-3, Gemini or similar benchmark models.

Agent Frameworks: Proven ability to design and build intelligent agents using LLM and other AI techniques using Autogen, CrewAI, Autotrain, Langchain, Llama       Index

Programming: Proficiency in Python and experience with deep learning libraries like TensorFlow or Pytorch.

Cloud computing: Experience with AWS services like EC2, SageMaker, Lambda

Data pipeline technologies like Apache Kafka, AWS Kinesis and data storage solutions e.g. (S3, Azure Blob Storage)

Knowledge of MLOps practices and tools (e.g.: Kubeflow, MLFlow, SageMaker, Azure ML)

Rest API development using Flask or Django

Basic AI/ML skills

Python Libraries like Matplotlib, SciPy, Pandas

Pattern recognition using ML models, CNN, Time-Series modelling, and custom trained Transformer architectures.

Responsibilities:

Agent Development: Design and develop intelligent agents that can interact with users, understand complex tasks, and generate human like responses using LLMs and other AI techniques.

Custom LLM development: Finetune and customize existing LLMs or build new models from scratch to address specific business needs and domains.

Framework Expertise: Utilize and contribute to open-source or internal frameworks for LLM development, deployment, and management.

Application development: Build applications that leverage generative AI capabilities, including chatbots, text generation tools, code generation tools and more.

Deployment and Infrastructure: Deploy and manage AI services on AWS, ensuring scalability, reliability, and security.

Collaboration:   Work closely with cross functional teams, including data scientists, software engineers, and product managers, to understand requirement and develop successful AI solutions.

Research and Development: Stay updated on latest advancements in generative AI and explore new technologies and techniques to improve the performance and capabilities of our AI systems.

Requirements:

Experience: 3-5 years of experience in developing and deploying AI solutions with a focus on generative AI and LLMs

LLMs:    Strong understanding of LLM architectures, training models, and applications. Experience with popular LLMs such as GPT-4, Llama-2, Llama-3, Gemini or similar benchmark models.

Agent Frameworks: Proven ability to design and build intelligent agents using LLM and other AI techniques using Autogen, CrewAI, Autotrain, Langchain, Llama       Index

Programming: Proficiency in Python and experience with deep learning libraries like TensorFlow or Pytorch.

Cloud computing: Experience with AWS services like EC2, SageMaker, Lambda

Data pipeline technologies like Apache Kafka, AWS Kinesis and data storage solutions e.g. (S3, Azure Blob Storage)

Knowledge of MLOps practices and tools (e.g.: Kubeflow, MLFlow, SageMaker, Azure ML)

Rest API development using Flask or Django

Basic AI/ML skills

Python Libraries like Matplotlib, SciPy, Pandas

Pattern recognition using ML models, CNN, Time-Series modelling, and custom trained Transformer architectures.