Lead AI Architect/Strategist
L
LTSAI Architecture
United States - RemoteFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 10+ years of experience in software engineering, enterprise architecture, solution architecture, data architecture, or technology transformation; 5+ years of experience designing and implementing AI/ML solutions, AI platforms, or intelligent automation solutions.
- Required Skills
- PythonMLOpsGenerative AILangChain
Requirements
- Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or related technical field.
- 10+ years of experience in software engineering, enterprise architecture, solution architecture, data architecture, or technology transformation.
- 5+ years of experience designing and implementing AI/ML solutions, AI platforms, or intelligent automation solutions.
- Strong experience architecting enterprise-scale technology solutions and distributed systems.
- Proven experience with Generative AI, Large Language Models (LLMs), and AI application architectures.
- Hands-on experience with Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, vector databases, and model orchestration.
- Experience with cloud AI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI.
- Experience with AI frameworks including Lang Chain, Lang Graph, Llama Index, Semantic Kernel, or OpenAI frameworks.
- Strong programming experience with Python.
- Understanding of MLOps/LLMOps practices including monitoring, evaluation, and deployment.
Responsibilities
- Define and execute AI architecture strategies, technical roadmaps, and modernization approaches aligned with organizational goals.
- Design scalable AI platforms and solutions leveraging Generative AI, LLMs, RAG, AI agents, machine learning, and automation capabilities.
- Architect intelligent AI systems that integrate models, data platforms, enterprise applications, and APIs.
- Develop architecture patterns and best practices for LLM-based applications, Agentic AI workflows, and data-driven decision support.
- Design and evaluate RAG architectures, including data ingestion, embeddings, vector search, retrieval strategies, and model orchestration.
- Establish AI governance practices supporting security, compliance, responsible AI, scalability, and operational excellence.
- Provide technical leadership, mentorship, and guidance on AI architecture patterns and engineering best practices.
- Communicate complex AI concepts and recommendations to technical teams, business stakeholders, and executive leadership.
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