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About This Role
Overview:
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
Qualifications:
Required Qualifications
- 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
What Success Looks Like
- 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.
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $184K across 1486 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At LMI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $175,000 based on 11,128 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,500. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
LMI AI Hiring
LMI has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span McLean, VA, US, Tysons, VA, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (98) are outnumbered by mid-level (1,283) and senior (1,092) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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