Enterprise AI Lead

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. Responsibilities: What You’ll Do

• 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, * AI capabilities are delivered as real, production-grade systems, not prototypes or isolated demos
• Teams can leverage reusable AI platforms and services to build and deploy solutions quickly
• AI systems are observable, reliable, and governed, with clear evaluation and risk controls
• Data pipelines and RAG architectures provide secure, high-quality inputs to AI systems
• AI adoption grows through usable tools, not mandates, driven by strong platform design
• New AI capabilities move rapidly from prototype to production

Why This Role Matters Most organizations struggle to move AI beyond experimentation. The Enterprise AI Lead changes that by building the platforms, pipelines, and applications that make AI usable in real operations. This role ensures that AI is not just a strategy, but a working capability embedded into systems, workflows, and decisions-delivered through strong engineering, practical architecture, and hands-on leadership. The target salary range for this position is $150,000-$190,000. The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances. Applicants must meet eligibility requirements for a U.S. Government security clearance. Only US Citizens are eligible for a security clearance. For this position, LMI will only consider applicants with security clearances or applicants who are eligible for security clearances, due to the nature of the work. Job Locations: US-VA-Tysons

• 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

Preferred Qualifications

• 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