AI Solutions Architect

SolomonEdwards is a national professional services firm offering financial, operational and technology consulting and operations support. Whether you need specialized consulting services or additional support to execute key initiatives, we bring the right people and expertise together to turn business challenges into value-creation opportunities. Role Summary The AI Solutions Architect serves as the technical subject-matter expert responsible for designing, evaluating, and scaling secure artificial intelligence solutions. You will bridge raw data science with production software engineering, establishing reusable architectural patterns, guiding vendor evaluations, and ensuring robust governance and compliance. Key Responsibilities Design secure, scalable, and cost-effective end-to-end AI and Machine Learning architectures. Build and deploy Retrieval-Augmented Generation (RAG) frameworks and autonomous/semi-autonomous AI agents. Integrate core AI capabilities smoothly with major cloud environments like AWS and Microsoft Azure. Utilize AI-assisted coding tools to accelerate agile software development, testing, and system documentation. Governance, Security & Evaluation Establish proof-of-concept (PoC) success criteria and systematically evaluate third-party AI solutions and vendor tools. Oversee MLOps and LLMOps pipelines to monitor production performance, drift, latency, and hallucinations. Construct analytical dashboards and reporting models to track AI adoption, operational outcomes, and cost-efficiency. Ensure strict adherence to enterprise data privacy, security frameworks, and responsible AI guidelines. Provide technical leadership and mentorship to data scientists, software engineers, and cross‑functional technology teams. Translate high‑level business targets into tactical implementation roadmaps. Communicate complex engineering concepts and risk assessments clearly to technical and executive stakeholders. Required Qualifications Education: Undergraduate degree in Computer Science, Data Science, Artificial Intelligence, or a related technical discipline. Experience: 5+ years in software/solution architecture with a focused track record in machine learning or deep learning. GenAI & Agents: 2+ years of hands‑on experience with commercial LLMs (ChatGPT, Claude, Microsoft Copilot) and building RAG/agentic pipelines. Cloud Platforms: 1+ years configuring and integrating AI infrastructure across AWS or Azure. Operations: Proven history defining AI Key Performance Indicators (KPIs), monitoring metrics, and building adoption dashboards. Soft Skills: Advanced technical writing, stakeholder communication, and public speaking abilities. Preferred Qualifications Advanced Degree: Master’s or Ph.D. in Artificial Intelligence, Computer Science, or related quantitative field. Foundational Knowledge: Deep understanding of neural network topologies, transformer architectures, and NLP principles. Data Engineering: Practical familiarity with database cleaning, unstructured data pipelines, and vector databases. #J-18808-Ljbffr