GEN AI

P1-C3-STS

JD

Experience: 12+ overall IT, 5+ in architecture, 3+ in AI/ML

Overview

AI Solutions Architect Role to drive AI innovation at a major aluminum manufacturing client focused on helping shape strategy, evaluate GenAI and ML tools, and design solutions that bring real business value. This role requires strong technical expertise, business acumen, and the ability to work with both senior executives and technical teams.

Key Responsibilities

Architect AI/ML and GenAI solutions for manufacturing and supply chain use cases like predictive maintenance, quality assurance, production optimization, and worker safety.

Evaluate and recommend LLMs (open-source and commercial), tools for RAG (Retrieval-Augmented Generation), Agentic AI, vector databases, and orchestration frameworks like LangChain or Semantic Kernel.

Stay current with trends like fine-tuning, prompt engineering, model compression, low-rank adaptation (LoRA), and synthetic data generation.

Understand security, privacy, and compliance concerns related to AI, including data protection laws (e.g., GDPR), model misuse, and global regulatory trends like the EU AI Act.

Lead assessments, POCs, and design reviews ask the right questions, challenge assumptions, and drive actionable decisions.

Align AI solutions with enterprise architecture principles security, scalability, integration, and governance.

Collaborate with data, app, and business teams to ensure robust pipelines, MLOps, and lifecycle management for deployed models.

Understand and address manufacturing specific concerns like downtime, latency, uptime, process precision, and compliance.

Qualifications

12+ years of overall experience in IT

5+ years in solution architecture roles experience across both business-facing and technical domains.

3+ years hands on experience with AI/ML, including GenAI use cases.

Deep familiarity with open-source LLMs (e.g., LLaMA), RAG architecture, and agent frameworks.

Experience with Azure OpenAI, Azure Machine Learning, and related tools in the Microsoft ecosystem.

Exposure to smart manufacturing or industrial AI use cases (e.g., computer vision in QA, anomaly detection, supply chain AI).

Strong communication skillsable to work with senior management, business, product, and engineering teams.

Some background in enterprise architecture (e.g., TOGAF, Zachman framework) is a plus.