Principal architect- machine learning

4 days ago

Bangalore, 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, Step Functions, Sage Maker 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 ( LLa MA, 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, Lang Chain, Llama Index, 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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