Associate Architect – Machine Learning

Role Overview

• Work as an Associate Architect
• Machine Learning (AWS)
• Collaborate with cross-functional teams to deliver cloud ML solutions
• Implement and develop machine learning models on AWS
• Optimize and evaluate machine learning workflows and models
• Design and maintain software architecture for cloud-based applications

Requirements

• 7
• 13 years of experience
• 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
• Experience using GenAI frameworks such as AWS Bedrock, OpenAI
• Hands-on experience fine-tuning large language models( LLM) and Generative AI (GAI), specifically LLama2
• 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
• Experience with Deep Learning Concepts
• Transformers, BERT, Attention models
• Experience with Prompt Engineering: Engineer prompts and optimizes 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
• Thorough understanding of NLP techniques for text representation and modeling
• Ability 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.

Tech Stack

• Airflow
• AWS
• Cloud
• ElasticSearch

Benefits

• A culture built on transparency, diversity, integrity, learning and growth
• Ample opportunities to learn, grow and interact with colleagues from varied experience and backgrounds around the globe.