
Data Science Leader
5 days ago
Principal Architect - Machine Learning
The Principal Architect - Machine Learning will serve as a trusted technical advisor for customers, addressing complex technical challenges pertaining to AI/ML opportunities.
They will provide expertise in the architecture, design, and development of solutions within AWS, collaborating with internal teams and external stakeholders to design optimized solutions on AWS Cloud.
Key responsibilities include working with the pre-sales team on RFP, RFIs, and helping them solutioning for different AI/ML use cases, strong analytical skills to evaluate scenarios and use cases, offering potential solutions for AI/ML implementations.
Additionally, they will stay up-to-date with the latest advancements in Generative AI and Machine Learning, demonstrated problem-solving, communication, and organizational skills, a positive attitude, and the proven ability to negotiate and influence others to obtain desired results.
Required SkillsAWS Cloud and AWS Machine Learning Capabilities
- Experience: 14+ years
- Well-versed with AWS Cloud and AWS Machine Learning capabilities and offerings:
- Proven experience using AWS Sagemaker leveraging different types of data sources,
- Training jobs, real-time and batch Inference, and Processing Jobs.
- Hands-on experience of working with Sagemaker studio, canvas, and data wrangler.
- Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
- Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.
- Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, 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
- Understanding of LLM architectures ( LLaMA, Claude, Amazon Nova etc.), with a focus on their training and inference workflows
- Expertise in designing, fine-tuning, and deploying generative AI models and building agentic workflows.
- Experience with prompt engineering and optimization techniques to improve LLM outputs for specific business use cases
- Good Understanding of open-source LLM frameworks and libraries (e.g., Hugging Face Transformers, LangChain, LlamaIndex, Haystack)
- Great analytical skills, with expertise in analytical toolkits such as Logistic Regression, Cluster Analysis, Factor Analysis, Multivariate Regression, Statistical modeling, predictive analysis
- Experience in leveraging AWS Lambda/API Gateway services for AI/ML model consumption and inferences. Hands-on experience with Dev Ops(CICD) & ML Ops services/tools.
- Must have led teams of ML Engineers in end-to-end production deployment for projects.
- Strong understanding of data privacy, compliance, and responsible AI practices while building and deploying LLM solutions in production environments
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