Lead AI Architect/Strategist

LTS

18d

About The Position

LTS is seeking a Lead AI Architect / Strategist to define and lead the architecture, strategy, and implementation of next-generation AI solutions. This role will drive enterprise AI transformation by designing scalable AI platforms, enabling Generative AI capabilities, and guiding the adoption of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent agents, and advanced analytics solutions. The ideal candidate is a strategic technology leader with deep expertise in AI architecture, cloud platforms, enterprise systems, and data transformation who can bridge business objectives with innovative technical solutions. This individual will work across engineering, data, product, cybersecurity, and leadership teams to establish AI roadmaps, architecture standards, and secure AI solutions that deliver measurable mission and business value.

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 AI concepts and technologies including Retrieval-Augmented Generation (RAG), AI Agents / Agentic AI, prompt engineering, vector databases and embeddings, model orchestration, AI governance and lifecycle management
Experience with cloud AI platforms such as: Azure OpenAI / Azure AI Services, AWS Bedrock, Google Vertex AI
Experience with modern AI frameworks and tools such as: Lang Chain, Lang Graph’ Llama Index, Semantic Kernel, OpenAI frameworks
Strong programming experience with Python or other modern programming languages.
Experience with data architecture, APIs, data pipelines, and enterprise system integration.
Understanding of MLOps/LLMOps practices, including monitoring, evaluation, deployment, and governance.
Ability to communicate complex technical concepts effectively with both technical and non-technical audiences.

Nice To Haves

Experience supporting Federal Government, healthcare, or other highly regulated environments.
Experience designing secure AI solutions within enterprise environments.
Experience with Kubernetes, containerized applications, cloud-native architectures, and modern DevOps practices.
Experience with enterprise architecture frameworks such as TOGAF.
Experience leading AI transformation initiatives from strategy through implementation.

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, AI copilots and automation solutions, knowledge retrieval systems, data-driven decision support
Design and evaluate RAG architectures, including data ingestion, embeddings, vector search, retrieval strategies, and model orchestration.
Lead technical evaluations of emerging AI technologies, frameworks, and platforms.
Guide teams in implementing AI solutions from proof-of-concept through production deployment.
Establish AI governance practices supporting security, compliance, responsible AI, scalability, and operational excellence.
Collaborate with software engineers, data scientists, product teams, and enterprise architects to deliver AI-enabled solutions.
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.
Support innovation initiatives through prototypes, technical demonstrations, and AI transformation strategies.