AI Solution Development & Deployment Specialist

Role Overview:We are seeking a highly skilled AI Solution Development & Deployment Specialist to design, build, and operationalize AI solutions across our business units. This role blends AI architecture, hands‑on development, and business engagement, enabling the company to accelerate innovation, improve operational efficiency, and deploy intelligent agents that support teams across engineering, design, HR, manufacturing, supply chain, quality, and customer experience. You will work closely with end users to understand their needs, translate them into AI use cases, and deliver production‑ready solutions using platforms such as Azure AI, OpenAI, Microsoft Copilot Studio, and other enterprise AI tools.Key Responsibilities: 1. AI Solution Architecture & Design • Design end‑to‑end AI architectures for use cases across all business functions.• Select appropriate models, frameworks, and platforms (Azure AI, Azure ML, OpenAI, LangChain, vector databases, etc.). • Define data pipelines, integration patterns, and deployment strategies. • Ensure solutions meet enterprise standards for security, scalability, and performance. 2. AI Development & Engineering • Build AI agents, copilots, and automation workflows using Azure AI Studio, Copilot Studio, and custom code. • Develop and fine‑tune models for NLP, computer vision, predictive analytics, and generative AI. • Implement retrieval‑augmented generation (RAG) pipelines and knowledge bases.• Integrate AI solutions with enterprise systems (ERP, MES, PLM, CRM, ClickUp, etc.). • Conduct testing, validation, and optimization of AI models.3. User Engagement & Requirements Gathering • Work directly with end users to understand pain points, workflows, and business processes. • Translate user needs into clear AI use cases and technical requirements. • Facilitate workshops, discovery sessions, and rapid prototyping. • Communicate complex AI concepts in simple, business‑friendly language. 4. Deployment, Monitoring & Continuous Improvement• Deploy AI solutions into production environments following MLOps best practices. • Monitor model performance, drift, and user adoption. • Implement feedback loops and continuous improvement cycles. • Ensure compliance with data governance, privacy, and ethical AI guidelines.5. Documentation, Case Studies & Knowledge Sharing • Create clear documentation, user guides, and training materials. • Build internal case studies showcasing business impact and ROI. • Present solutions to leadership and cross‑functional teams. • Contribute to the company’s AI roadmap and capability development.6. AI Infrastructure Understanding & Specification • Assess and define the AI infrastructure required to support solution development and deployment, including compute resources, storage, networking, and security needs. • Collaborate with IT, cloud, and cybersecurity teams to specify scalable and cost‑efficient AI environments (Azure AI, Azure ML, containerized workloads, GPU clusters, vector databases, etc.). • Ensure the underlying infrastructure supports model training, inference, monitoring, and lifecycle management. • Recommend improvements to the AI platform ecosystem to enhance performance, reliability, and developer productivity. • Evaluate emerging AI infrastructure technologies and advise on adoption strategies. Required Qualifications:• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or related field. • 3–7 years of experience in AI/ML development, solution architecture, or applied data science. • Strong hands‑on experience with Azure AI, Azure Machine Learning, OpenAI APIs, or similar platforms. • Proficiency in Python and modern AI frameworks (PyTorch, TensorFlow, HuggingFace, LangChain). • Experience building AI agents, chatbots, or copilots.• Strong understanding of data engineering concepts and cloud‑native architectures.• Ability to translate business problems into technical solutions. • Excellent communication and stakeholder‑management skills. Preferred Qualifications:• Experience in manufacturing, automotive, or EV industry. • Knowledge of MLOps tools (Azure DevOps, MLflow, Kubernetes). • Experience with computer vision (quality inspection, defect detection). • Familiarity with RAG, vector databases (Pinecone, Azure Cognitive Search), and prompt engineering. • Experience building dashboards, automations, or workflows in ClickUp, Power Platform, or similar tools.