AI Engineer

Strategic Advising: Provide guidance on a wide range of technology, strategy, and policy issues related to AI. Design and Development: Collaborate with the engineering and leadership teams on functional design, process design (including scenario design and flow mapping), prototyping, testing, training, and defining support procedures. Solution Architecture: Document and articulate the solutions architecture and lessons learned from each project and accelerated incubation. Market Assessment: Lead a team in evaluating the AI and automation market and competitor landscape. Stakeholder Liaison: Act as a bridge between stakeholders and project teams, delivering feedback and enabling necessary changes in product performance or presentation. AI Software Development: Model, build, and test AI software to handle large datasets and achieve desired outcomes. Programming Proficiency: Utilize programming languages such as Python, Java, C++, or others commonly used in AI development. Data Analysis: Analyze statistics, data, and algorithms to ensure projection accuracy. Strategic Recommendations: Use AI analysis to make strategy recommendations that align with company goals. Continuous Learning: Stay updated on AI knowledge, trends, and regulations. Progress Reporting: Collect, organize, and present progress updates to team leadership and stakeholders. Proven experience in developing and deploying AI models. Experience with natural language processing (NLP) or computer vision. Proficiency with deep learning frameworks such as TensorFlow or PyTorch. Strong understanding of machine learning (ML). Knowledge of basic algorithms, object-oriented and functional design principles, and best-practice patterns. Experience in REST API development, NoSQL database design, and RDBMS design and optimizations. Familiarity with cloud platforms such as AWS, Google Cloud, or Azure is a plus. Experience in program leadership, governance, and change enablement.