AI Architect

Job description

Job Summary: We are seeking a technical and consultative Lead AI Solutions Engineer to join our IT Services team for a global manufacturing client. In this high-impact role, you will act as the strategic right-hand to the AI Lead; capturing visionary "loud thinking," converting abstract ideas into actionable blueprints, and building production-grade solutions. You will bridge the gap between technical execution and business strategy: evaluating incoming AI requests, establishing secure experimentation guardrails on Azure and Databricks, and delivering scalable AI applications across GenAI, Agentic AI, and classical ML. Key Responsibilities: Strategic Execution & Blueprinting: Partner closely with the AI Lead to synthesize strategic goals into clear technical architectures, roadmaps, and execution plans. Define end-to-end AI project lifecycles from proof-of-concept (PoC) to full production deployment. Use Case Triage & Business Consultation: Evaluate business requests from the manufacturing user community; filter hype from high-value, credible AI use cases. Guide business stakeholders on AI feasibility, ROI, risk, and expected outcomes with confidence and clarity. Hands-on Development & Deployment: Design, build, test, and deploy robust AI solutions spanning Generative AI, Agentic workflows, and traditional machine learning models. Integrate solutions seamlessly within Microsoft Azure and Databricks ecosystems. Infrastructure, Guardrails & Experimentation: Provision and manage the required Azure/Databricks cloud infrastructure to enable safe sandbox experimentation for users. Implement governance, security protocols, Responsible AI guardrails, cost-tracking, and telemetry across all AI deployments. Stakeholder Management: Communicate complex technical concepts effectively to non-technical business leaders and operational teams. Drive alignment across cross-functional enterprise teams, including IT, Data Engineering, Security, and Business Operations. Technical Expertise AI & GenAI: Deep understanding of Machine Learning fundamentals, Deep Learning, Large Language Models (LLMs), Fine-Tuning, RAG (Retrieval-Augmented Generation), and Agentic Frameworks (e.g., LangChain, AutoGen, CrewAI, Semantic Kernel). Cloud Platform: Advanced hands-on experience with Microsoft Azure (Azure OpenAI Service, Azure ML, Azure Functions, Azure Cosmos DB/Vector Stores). Data Engineering: Strong proficiency in Databricks (PySpark, Delta Lake, MLflow, Unity Catalog) for data processing and model deployment. DevOps/MLOps: Experience setting up CI/CD pipelines, containerization (Docker, Kubernetes), and monitoring for AI workloads. Core Competencies Consultative & Analytical Mindset: Strong capability to dissect hype, assess technical feasibility, and prioritize business impact. Communication: Exceptional verbal and written communication skills to manage stakeholders, lead technical reviews, and articulate complex solutions clearly. Preferred Experience Proven track record of working on Microsoft Azure and Databricks platforms. 5+ years of experience in Data Science, Machine Learning, or AI Engineering. 2+ years of hands-on experience designing and deploying GenAI/Agentic solutions in enterprise environments.