Job
Description
Job Summary: We are seeking an experienced AI Solution Architect to lead the design and implementation of scalable AI/ML systems and solutions. The ideal candidate will bridge the gap between business needs and technical execution, translating complex problems into AI-powered solutions that drive strategic value. Key Responsibilities: Solution Design & Architecture Design end-to-end AI/ML architectures, integrating with existing enterprise systems and cloud infrastructure. Lead technical planning, proof-of-concepts (POCs), and solution prototyping for AI use cases. AI/ML System Development Collaborate with data scientists, engineers, and stakeholders to define solution requirements. Guide the selection and use of machine learning frameworks (e.g., TensorFlow, PyTorch, Hugging Face). Evaluate and incorporate LLMs, computer vision, NLP, and generative AI models as appropriate. Technical Leadership Provide architectural oversight and code reviews to ensure high-quality delivery. Establish best practices for AI model lifecycle management (training, deployment, monitoring). Advocate for ethical and responsible AI practices including bias mitigation and model transparency. Stakeholder Engagement Partner with business units to understand goals and align AI solutions accordingly. Communicate complex AI concepts to non-technical audiences and influence executive stakeholders. Innovation & Strategy Stay updated on emerging AI technologies, trends, and industry standards. Drive innovation initiatives to create competitive advantage through AI. Required Qualifications: Bachelor s or Master s in Computer Science, Engineering, Data Science, or related field. 6 years of experience in software development or data engineering roles. 3+ years of experience designing and delivering AI/ML solutions. Proficiency in cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes, Docker). Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Strong programming skills in Python and familiarity with CI/CD pipelines. Preferred Qualifications: Experience with generative AI, foundation models, or large language models (LLMs). Knowledge of data privacy regulations (e.g., GDPR, HIPAA) and AI compliance frameworks. Certifications in cloud architecture or AI (e.g., AWS Certified Machine Learning, Google Professional ML Engineer). Strong analytical, communication, and project management skills.
