| Machine Learning Architect at Remote, Remote, USA |
| Email: [email protected] |
| From: Santhoshi, HAN IT Staffing [email protected] Reply to: [email protected] Role: Machine Learning Architect - Onsite Client : Capgemini Work location: DALLAS (US:75201), TX JOB DESCRIPTION: Job Summary. Machine Learning Architect Job Description Overview The Machine Learning Architect is a technical leader responsible for designing, building, and maintaining scalable, high-performing machine learning (ML) systems. This role bridges the gap between business needs and technical feasibility, ensuring ML initiatives align with overall company strategy. Responsibilities: Design and develop the architecture for ML platforms and solutions. Select and implement appropriate ML algorithms and tools based on business requirements and data characteristics. Collaborate with data scientists, data engineers, and software engineers to ensure the successful integration of ML models into production systems. Oversee data pipelines and ensure data quality for training and deploying ML models. Monitor and evaluate the performance of ML models, identifying opportunities for improvement and retraining. Stay up-to-date on the latest advancements in ML research and best practices. Develop and implement strategies for managing model bias and ensuring the ethical use of AI. Communicate complex technical concepts to both technical and non-technical stakeholders. Mentor and guide junior team members on ML architecture best practices. Qualifications: Master's degree in Computer Science, Engineering, or a related field (or equivalent experience). Strong understanding of machine learning concepts, algorithms, and frameworks. Experience designing and developing scalable, distributed systems. Experience with cloud platforms (AWS, Azure, GCP) is a plus. Excellent communication and collaboration skills. Proven ability to translate business goals into technical requirements. Experience working in an Agile development environment is a plus. Tools and Technologies (possible, depending on the company): Python (Scikit-learn, TensorFlow, PyTorch) Machine Learning Platforms (AWS SageMaker, Azure Machine Learning, Google AI Platform) Cloud Computing Technologies (AWS, Azure, GCP) Docker, Kubernetes DevOps Tools (Git, Jenkins, CI/CD pipelines). Keywords: continuous integration continuous deployment artificial intelligence machine learning information technology Texas Machine Learning Architect [email protected] |
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| Wed May 01 21:15:00 UTC 2024 |