Enterprise AI Lead

Logistics Management Institute•Tysons, VA

3d•$150,000 - $190,000

About The Position

We are looking for an Enterprise AI Lead to design, build, and scale AI capabilities across the organization. This is a hands-on leadership role focused on developing real systems—not just strategy— spanning AI platforms, data pipelines, and production-grade AI applications. You will operate at the intersection of AI platform engineering, data architecture, and solution delivery, leading by building and establishing the technical foundation for enterprise AI. This includes everything from LLM platforms and agent orchestration to MLOps, RAG pipelines, and AI-enabled applications. This role is ideal for someone with a platform engineering or infrastructure background who has moved into AI and wants to continue building—while also shaping strategy, standards, and long-term direction. LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed. Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

Requirements

Strong experience building and operating platforms or infrastructure systems, with a shift into AI/ML or data platforms
Hands-on experience developing and deploying AI/LLM-based systems in production
Experience with LLMs, RAG architectures, embeddings, and agent-based systems
Experience building or operating AI/LLM platforms, internal developer platforms, or shared services
Strong experience with data engineering and pipeline development
Experience with MLOps practices, including model lifecycle management, deployment, and monitoring
Proficiency in backend development (Python, Node.js, or similar) and API design
Experience working in cloud environments (AWS, Azure, or GCP) with distributed systems
Strong understanding of system design, scalability, and operational reliability
Familiarity with secure or regulated environments and data protection requirements
Ability to operate both hands-on as a builder and strategically as a technical leader

Nice To Haves

Background in platform engineering, DevSecOps, or infrastructure engineering
Experience designing multi-tenant AI platforms or enterprise AI services
Familiarity with agent orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar
Experience with vector databases and semantic search systems
Experience implementing AI governance, guardrails, and model assurance practices
Familiarity with secure or regulated environments and data protection requirements
Experience integrating AI into enterprise applications, workflows, or operational systems
Experience supporting analytics platforms, data warehouses, or enterprise BI systems

Responsibilities

Design and build enterprise AI/LLM platforms, including model access layers, orchestration, prompt management, and evaluation capabilities
Develop and deploy AI agents and orchestration frameworks to automate workflows and enable intelligent system behavior
Architect and implement RAG pipelines and secure data integration patterns, connecting enterprise data to AI systems
Build and operate MLOps pipelines supporting model deployment, monitoring, evaluation, and lifecycle management
Develop production-grade AI-enabled applications and services, integrating AI into real operational workflows
Define and implement AI strategy and governance with a focus on practical, enforceable standards
Establish model assurance and risk management practices, including evaluation frameworks, guardrails, and observability
Build and maintain operational data pipelines to support AI and analytics workloads
Integrate AI capabilities into enterprise platforms, APIs, and business systems
Lead rapid AI prototyping and experimentation, turning emerging capabilities into deployable solutions
Build and evolve an AI enablement platform, including reusable services, implementation playbooks, guardrails, and a shared knowledge base, enabling teams to adopt AI capabilities consistently and efficiently.
Enable internal teams through reusable platform services, templates, and development patterns
Contribute to enterprise BI and analytics capabilities, integrating AI-driven insights into decisionmaking workflows