6+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.
Hands-on experience on AWS Machine Learning services. Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs.
Good Experience developing applications using LLMs with Langchain.
Must have experience using GenAI frameworks such as vertexAI, OpenAI, AWS Bedrock.
Must have Hands-on experience fine-tuning large language models( LLM) and Generative AI (GAI), specifically LLama2.
Must have Hands-on experience working with (Retrieval Augmented Generation) RAG architecture and experience using vector indexing such as Opensearch, Elasticsearch.
Strong familiarity with higher-level trends in LLMs and open-source platforms.
Should have experience with Deep Learning Concepts. Transformers, BERT, Attention models
Prompt Engineering: Engineer prompts and optimize few-shot techniques to enhance LLMs performance on specific tasks, eg personalized recommendations.
Model Evaluation Optimization: Evaluate LLMs zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
Response Quality: Collaborate with ML and Integration engineers to leverage LLMs pre-trained potential, delivering contextually appropriate responses in a user-friendly web app.
Implement and manage MLOps principles and best practices for Gen AI models
Thorough understanding of NLP techniques for text representation and modeling
Able to effectively design software architecture as required
Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etcKnowledge of a variety of machine learning techniques (Supervised/unsupervised etc) (clustering, decision tree learning, artificial neural networks, etc) and their real-world advantages/drawbacks
Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc
Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.

Good to have skills:

Experience of working for customers/workloads in the Edtech domain with use cases.
Experience with software development

Associate Architect - Machine Learning (AWS)