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About This Role
Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
Work You'll Do
The Assessments Liaison Officer (LNO) serves as the Deputy Chief for Security Cooperation within the Strategic Planning and Policy division, acting as a key advisor and integrator for all security cooperation (SC) activities. This role provides strategic guidance and expert advice to the Headquarters Element Commander, Chief of Staff, Planning and Policy leaders, agency directors, and subordinate command structures. The LNO bridges operational security cooperation efforts with long-range policy and strategic objectives to build access, reinforce alliances, and enhance partner capacities across the Area of Responsibility (AOR).
Key Responsibilities
- Strategic Security Cooperation Advisement
o Advise senior leadership on all aspects of security cooperation program activities, initiatives, and bilateral/multilateral agreements.
o Shape policy inputs and make recommendations to align SC activities with organizational goals and evolving global or regional challenges.
? Example: Advise on implementation of a new regional capacity-building exercise, ensuring alignment with both U.S. objectives and partner priorities.
- OAIs Integration and Policy Planning
o Lead the integration of security cooperation operations, activities, and investments (OAIs) into policy and posture planning efforts to maximize their impact.
o Collaborate with planners and policy advisors to ensure SC efforts dovetail with broader strategic guidance, theater posture reviews, and contingency planning.
? Example: Integrate a partner nation military modernization program with ongoing posture adjustments to enable shared interoperability and readiness.
- Building Partner Capacity Expertise
o Serve as the focal point for building partner capacity, offering guidance to commanders, staff principals, subordinate elements, and Security Cooperation Offices (SCOs) on best practices and opportunities for partnership enhancement.
o Deliver advice and lessons learned from successful past programs such as multinational training efforts or logistics-sharing initiatives.
- Metrics & Outcomes Management
o Develop and track key metrics for SC program effectiveness-such as increased partner nation capabilities, frequency and sophistication of joint exercises, and improvements in information/intelligence sharing.
o Provide assessments to leadership that inform decision-making and resource allocation.
Potential Scenarios & Navigation
- Regional Crisis Response
o Navigate complex multinational coordination for a joint humanitarian operation, leveraging established SC agreements to provide immediate aid and operational support.
o Propose and facilitate rapid mobilization of pre-positioned supplies and forces from partner nations.
- Bilateral Partnership Negotiation
o Lead policymakers through the negotiation or renewal of critical bilateral logistics agreements, ensuring legal compliance, mutual benefit, and the advancement of strategic access.
- Partner Capacity Shortfalls
o Identify and address gaps in partner nation forces' capabilities, designing targeted SC initiatives (such as technical training or adviser exchanges) to address shortfalls and measure progress.
The Team
Deloitte's Government & Public Services (GPS) practice - our people, ideas, technology and outcomes - is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.
Our Industry Solutions clients seek verticalized solutions that transform how they sell products, deliver services, generate growth, and fulfill mission-critical operations. The Industry Solutions offering delivers integrated business expertise with repeatable scaled technology solutions that are specifically engineered for each sector's IndustryAdvantageTM.
The Project Delivery Talent Model is designed for professionals with specialized skills that align to a current client need. Team members focus on delivering services to clients, without additional expectations related to business development or promotion. Their employment is tied to their role on a project, and they are eligible for a benefits package that is competitive for project delivery-focused professionals.
Qualifications
Required:
- Bachelor's degree in International Relations, Political Science, Security Studies, Public Administration, Defense Studies, or a related Field
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
- Active TS/SCI security clearance required
- 7+ years of experience in security cooperation, international military engagement, defense policy, foreign assistance programs, or allied/partner relationship management
- Demonstrated ability to analyze complex military or policy environments and synthesize insights for senior leadership
- Demonstrated understanding of cultural considerations in building and maintaining international partnerships
Preferred:
- Master's degree in a related field such as International Affairs, Security/Strategic Studies, Public Policy, or equivalent
- Completion of intermediate or senior-level PME, such as Command and Staff College or War College.
- Direct involvement with programs such as Acquisition and Cross-Servicing Agreements (ACSA), Mutual Logistics Support Requests (MLSR), or partner capacity-building initiatives
- Prior work with embassies, Security Cooperation Offices, or liaison roles with foreign defense officials
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $102,750 to $171,250.
Information for applicants with a need for accommodation: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html
Salary Context
This $102K-$171K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Deloitte, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($137K) sits 11% below the category median. Disclosed range: $102K to $171K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Deloitte AI Hiring
Deloitte has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Honolulu, HI, US. Compensation range: $171K - $171K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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