Principal Architect- Machine Learning

4 days ago

Bengaluru, India Quantiphi Full time

Role: Principal Architect - ML Location: Mumbai/Bangalore Experience: 14+ years Role & Responsibilities - Act as a trusted technical advisor for customers, addressing complex technical challenges pertaining to AI/ML Opportunities - Provide expertise in the architecture, design, and development of solutions within AWS - Collaborate with internal teams and external stakeholders to design optimized solutions on AWS Cloud - Work with the pre-sales team on RFP, RFIs and help them solutioning for different AI/ML use cases - Strong analytical skills to evaluate scenarios and use cases, offering potential solutions for AI/ML implementations - 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. - Ability to speak in business terms, as well as the ability to effectively communicate both internally and externally. - 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 communicate technical roadmap, challenges, and mitigation. Required Skills - 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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