AI Architect

Job Description: AI Architect

Location: St. Louis, MO (Preferred) / Remote

Mode of hire: C2H (6 Months)

Rate: $DOE/hr on C2H

Job Summary

We are seeking a visionary AI Architect to bridge the gap between complex generative AI capabilities and strategic business outcomes. You will be responsible for defining the blueprint of our next-generation intelligent systems, ensuring that AI initiatives are not just innovative, but scalable, ethical, and deeply integrated into our enterprise ecosystem.

Key Responsibilities

• Strategic Alignment: Partner with business leaders to identify high-impact GenAI use cases and translate them into robust architectural roadmaps.
• End-to-End Design: Architect comprehensive AI solutions, specifically focusing on LLM orchestration, retrieval-augmented generation (RAG) workflows, and vector database integration.
• Infrastructure Lead: Evaluate and select the right AI stack, prioritizing cloud-native AI services and managed model platforms.
• Governance & Ethics: Establish frameworks for AI safety, prompt security, and model explainability to ensure compliant and ethical deployments.
• Cross-Functional Collaboration: Lead squads of Data Engineers and ML Engineers to build seamless APIs and data pipelines that power AI-driven products.
• Continuous Innovation: Prototype and pilot emerging GenAI trends (e.g., Agentic workflows, fine-tuning techniques) to maintain a competitive edge.

Required Skills & Qualifications

• Experience: 10+ years in IT/Software Engineering, with 4+ years dedicated to AI/ML Solution Architecture.
• Core Technical Focus: Mastery of Generative AI and LLM frameworks (e.g., LangChain, LlamaIndex, or AutoGen).
• Data Engineering: Hands-on proficiency in Python and PySpark for large-scale data processing and feature engineering.
• Vector Databases: Proven experience implementing and optimizing Vector DBs (e.g., Pinecone, Milvus, Weaviate, or Azure AI Search).
• Cloud Ecosystems: Expertise in deploying AI services within AWS (SageMaker/Bedrock), Azure (OpenAI/Machine Learning), or Google Cloud Platform (Vertex AI).
• MLOps & LLMOps: Deep understanding of the model lifecycle, including versioning, monitoring "hallucinations," and prompt engineering optimization.
• Communication: Exceptional ability to present "ROI-driven" AI blueprints to non-technical stakeholders.

Preferred Technology Stack

• AI: GenAI, LLMs, Vector DBs, Semantic Kernels.
• Languages: Python, PySpark, SQL.
• Cloud: Azure AI Services or AWS AI/ML stack.