Description

You will design and implement end-to-end cloud machine learning solutions and generative AI architectures on AWS.

Responsibilities

• Design software architecture for model training, deployment, and retraining using native AWS services like SageMaker and Lambda.
• Build and optimize LLM applications using Langchain and Retrieval Augmented Generation (RAG) architectures.
• Fine-tune large language models such as Llama2 and implement MLOps principles for Generative AI models.
• Engineer prompts and optimize few-shot techniques to improve model performance and response quality.
• Collaborate with developers, QA, and project managers to translate requirements into technical solutions.

Required Skills

• 6+ years of hands-on experience developing cloud ML solutions on AWS.
• Deep expertise with AWS SageMaker, including training jobs, real-time/batch inference, and processing jobs.
• Practical experience with GenAI frameworks including AWS Bedrock, OpenAI, and VertexAI.
• Proven ability to implement RAG architectures using vector indexing with OpenSearch or Elasticsearch.
• Strong knowledge of LLM fine-tuning, Transformers, BERT, and attention models.
• Proficiency in NLP techniques for text representation and modeling.
• Experience with workflow orchestration tools such as Airflow, Step Functions, SageMaker Pipelines, or Kubeflow.
• Understanding of supervised and unsupervised learning, including clustering and artificial neural networks.
• Ability to evaluate model performance through zero-shot/few-shot capabilities and hyperparameter tuning.

Preferred Skills

• Experience with software development.
• Experience working with Edtech domain workloads and use cases.

Key Skills

Education

Any Graduate