AI Tech Lead

The AI Solution Architect is responsible for designing and overseeing the implementation of AI solutions that align with business objectives and technology strategies. This role involves close collaboration with cross-functional teams, including data scientists, AI engineers, and business stakeholders, to ensure effective integration of AI technologies. The AI Solution Architect will provide technical leadership, define architecture standards, and ensure scalability, security, and maintainability of AI systems. Key Responsibilities AI Solution Design Lead the design and architecture of AI solutions in line with business goals and tech strategies. Develop end-to-end AI architectures including data collection, model development, deployment, and system integration. Collaborate with data scientists and engineers to define model requirements and integrate them into overall architecture. Technical Leadership Guide AI development teams with best practices in model development, data engineering, and system integration. Stay updated on latest AI tools and frameworks and integrate them where appropriate. Lead technical discussions and architecture decision-making. Stakeholder Collaboration Understand business needs and translate them into AI-driven solutions. Act as a bridge between technical and business teams. Present AI designs and architecture, addressing feedback and concerns. Scalability and Performance Design scalable AI architectures to manage high data volumes and complex computations. Optimize models and systems for efficient production performance. Implement monitoring and tuning strategies to ensure system responsiveness. Security and Compliance Ensure AI designs follow security best practices including data protection, privacy, and ethical standards. Ensure regulatory compliance in areas like healthcare, finance, and data privacy. Conduct risk assessments and define mitigation plans. Integration and Deployment Oversee AI integration with existing systems for seamless interoperability. Define and implement CI/CD pipelines for model deployment. Work with DevOps teams to automate deployments and monitor production AI systems. Documentation and Knowledge Sharing Create detailed documentation for AI architectures, data flows, and design decisions. Foster a culture of learning by sharing best practices and conducting workshops. Educate stakeholders on AI technology and applications. Continuous Improvement Assess post-deployment performance of AI solutions and identify improvements. Lead retrospectives and lessons-learned sessions. Drive AI innovation by exploring new use cases and applications. Qualifications Education Bachelor's in Computer Science, Engineering, Data Science, or related field. Master's or relevant certifications (e.g., AWS Certified Machine Learning, Google ML Engineer) preferred. Experience 7+ years in software architecture/design, including 3+ years in AI/ML solutions. Strong hands-on background in AI/ML frameworks like TensorFlow, PyTorch, scikit-learn. Experience in deploying AI solutions in cloud environments (AWS, Azure, GCP). Skills Comprehensive understanding of AI/ML lifecycle: data preprocessing, model training/validation, deployment, monitoring. Strong in data engineering and ETL/data pipeline design. Proficiency in DevOps: CI/CD, Docker, Kubernetes, Git. Excellent problem-solving and complex technical challenge resolution. Strong communication skills to interface with both technical and non-technical stakeholders. Preferred Experience in AI ethics, bias detection, fairness, and transparency. Knowledge in NLP, computer vision, or reinforcement learning. Familiarity with Agile development methodologies.