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The AI/ML Solutions Architect will lead the design, development, and deployment of advanced AI/ML solutions. This role combines deep technical expertise with strategic thinking to ensure AI/ML initiatives are successfully integrated into business operations. You will work closely with data scientists, engineers, and stakeholders to create architectures that maximize performance, scalability, and reliability.
Job Responsibility:
Design end-to-end AI/ML architectures, including data pipelines, model training, deployment, and monitoring
Collaborate with stakeholders to define AI/ML solution requirements aligned with business objectives
Provide technical leadership and guidance to teams implementing AI/ML models and systems
Develop scalable and secure solutions using cloud platforms (AWS, Azure, GCP) and MLOps best practices
Ensure seamless integration of AI/ML models into existing IT systems and workflows
Conduct feasibility studies, prototyping, and performance evaluations for new technologies and frameworks
Stay updated on advancements in AI/ML and recommend innovative solutions to meet emerging needs
Document technical designs, workflows, and implementation plans to ensure clarity and reproducibility
Requirements:
Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field (or equivalent experience)
5+ years of experience in AI/ML, software architecture, or cloud solutions engineering
Proficiency in AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, Scikit-learn)
Expertise in MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes)
Hands-on experience with big data tools and frameworks (e.g., Apache Spark, Hadoop)
Strong programming skills in Python, Java, or similar languages
Proficiency with cloud platforms (AWS SageMaker, GCP AI Platform, or Azure ML)
Familiarity with API development and microservices architectures
Excellent problem-solving, communication, and stakeholder management skills
Nice to have:
Certifications in cloud technologies (e.g., AWS Certified Machine Learning, Google Professional ML Engineer)
Experience with AI governance, ethical AI practices, and model interpretability tools (e.g., SHAP, Fairlearn)
Familiarity with Natural Language Processing (NLP) or Computer Vision frameworks
Knowledge of edge computing and deploying AI/ML models on IoT devices