Associate Technical Architect - ML

Basic Qualifications (BQ)

• 8+ 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 LLM's performance on specific tasks, e.g. personalized recommendations.
• Model Evaluation & Optimization: Evaluate LLM's 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 LLM's 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 etc.Knowledge 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.

What is in it for you