Architect - Machine Learning (AWS)

Job title: Architect - Machine Learning (AWS)

About the Role Quantiphi is seeking an Architect - Machine Learning (AWS) to design and deliver cloud-based ML solutions, focusing on GenAI and end-to-end deployment on AWS. This role involves building scalable architectures and collaborating across teams to deliver production-ready AI capabilities.

What You'll Do

• Design and implement cloud ML solutions on AWS (SageMaker) and GenAI workflows.
• Develop applications using LangChain and GenAI frameworks; work with LLMs including fine-tuning (e.g., LLama2).
• Architect Retrieval Augmented Generation (RAG) systems and implement vector indexing with OpenSearch/Elasticsearch.
• Build end-to-end ML lifecycle (training, deployment, retraining) using native AWS services (SageMaker, Lambda, etc.).
• Collaborate with ML and Integration engineers to deliver LLM-enabled web app experiences with contextual responses.
• Evaluate and optimize model performance (zero-shot, few-shot, hyperparameters) and ensure interpretability for production apps.
• Design software architecture for ML pipelines; work with workflow orchestration tools (Airflow, Step Functions, SageMaker Pipelines, Kubeflow).
• Stay current on LLM trends and guide cross-functional teams in implementing robust AI solutions.
• Nice-to-have: experience in Edtech domains; software development experience.

What We're Looking For

• 12+ years of hands-on experience implementing and developing cloud ML solutions on AWS.
• Proficiency with AWS ML services, especially SageMaker; experience with Sagemaker Training Jobs, real-time and batch inference, processing jobs.
• Hands-on experience with LangChain and GenAI frameworks; familiarity with AWS Bedrock and OpenAI.
• Hands-on experience fine-tuning large language models (LLMs) such as LLama2.
• Experience with Retrieval Augmented Generation (RAG) and vector indexing (OpenSearch, Elasticsearch).
• Strong knowledge of NLP, Transformers, BERT, attention models; prompt engineering and model evaluation.
• Ability to design end-to-end ML architectures using AWS (SageMaker, Lambda, etc.) and to collaborate with cross-functional teams.
• Experience with workflow orchestration tools: Airflow, Step Functions, SageMaker Pipelines, Kubeflow.
• Understanding of ML techniques (supervised/unsupervised, clustering, decision trees, neural networks).
• Excellent communication and collaboration skills; ability to translate requirements into scalable ML solutions.

Nice to Have

• Edtech domain experience; experience with software development.