AI Solution Development & Deployment Specialist

Role Overview

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.