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About This Role
About Us
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Since 1989, SHI International Corp. has helped organizations change the world through technology. We’ve grown every year since, and today we’re proud to be a $16 billion global provider of IT solutions and services.
Over 17,000 organizations worldwide rely on SHI’s concierge approach to help them solve what’s next. But the heartbeat of SHI is our employees – all 7,000 of them. If you join our team, you’ll enjoy:
- Our commitment to diversity, as the largest minority\- and woman\-owned enterprise in the U.S.
- Continuous professional growth and leadership opportunities.
- Health, wellness, and financial benefits to offer peace of mind to you and your family.
- World\-class facilities and the technology you need to thrive – in our offices or yours.
Job Summary
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The Principal Architect works across data engineering, data visualization, data governance, and AI rather than within a single technical area. On large, multi\-disciplinary engagements, this role orchestrates a unified technical approach across technical area leads, each of whom owns depth within their own discipline. Alongside that delivery leadership, this role carries the practice maturation agenda: the delivery standards, accelerators, engineering practices, evaluation rubrics, and advisory frameworks that make delivery consistent and repeatable as the practice scales. The Principal Architect may be billable on engagements where specific technical depth or cross\-discipline technical leadership is required.
Role Description
Cross\-Practice Delivery Leadership
- Lead the technical delivery approach on large, multi\-disciplinary engagements spanning data engineering, data visualization, data governance, and AI.
- Orchestrate a unified solution approach across capability leads, integrating each discipline’s technical direction into one coherent delivery plan with clear sequencing, dependencies, and shared standards.
- Own delivery approach rather than solution design within any single discipline: how the work is structured, sequenced, staffed, and de\-risked, and where the technical decision points sit.
- Identify delivery risk early and intervene on engagements drifting on approach, quality, or coherence across workstreams.
Practice Maturation
Priorities are set with the Practice Manager, each carried from assessment through to an adopted standard.
- Delivery Excellence: baseline delivery standards, reusable accelerators and templates, engineering practices informed by DORA capabilities, and adaptive delivery approaches that balance program predictability with the experimentation required by data and AI projects .
- Talent and Engagement Model: technical interview standards and calibration, a consistent hiring bar, skills inventory and workforce visibility, and engagement health practices that protect consultant focus.
- Scoping and Advisory Excellence: structured recommendation frameworks, strategic opportunity framing that translates vague asks into clarified business problems, and clearer engagement pathways between delivery, presales, and the PMO.
- Data and AI Fluency: capability assessment, targeted learning pathways mapped to assessment outcomes and to the pace of Microsoft platform change, and improved use of internal data for practice decision\-making.
- Build capabilities so they outlast the person driving them, with documented standards, named owners, and adoption evidence .
AI\-Accelerated Delivery
- Apply AI tooling and agentic patterns to how the practice delivers value , improving the speed, consistency, and quality of consulting work across all engagements .
- Evaluate where AI meaningfully changes delivery economics and where it introduces review overhead without net gain and build the evidence to distinguish the two.
- Fold proven patterns into delivery standards and accelerators so that gains are structural to the practice rather than dependent on individual habit .
Practice Leadership and Capability Growth
- Coach architects and consultants across disciplines on delivery approach, consulting judgment, and technical decision\-making. This is a player\-coach role without direct reporting relationships.
- Influence the priorities of technical leads where cross\-practice coherence, engagement risk, or the practice maturation agenda requires it.
Behaviors and Competencies
This role sits at the top of the technical career architecture and is expected to demonstrate expert\-level capability across the following.
- Subject Matter Expertise: Recognized depth in one discipline and sufficient working command of others to lead delivery, evaluate technical decisions, and identify risk outside their primary area.
- Facilitation: Leads discovery, architecture review, and executive working sessions to a decision, including where stakeholders disagree or the problem is poorly defined at the outset.
- Communication: Explains complex technical trade\-offs to executive, business, and technical audiences, and enables effective communication between groups that do not naturally align.
- Relationship Management: Builds durable trust with client executives, internal leadership, partners, and delivery teams, and sustains it through difficult engagements.
- Collaboration: Works in the open across technical disciplines , presales, PMO, and partner organizations, treating shared outcomes as the measure of success. Collaborates closely with Principal Architects from other delivery teams to enable consistent delivery approach and continuous improvement within SHI Services.
- Problem Solving: Anticipates systemic problems, addresses root causes rather than symptoms, and proposes solutions that hold up as the practice scales.
Skill Level Requirements
We are looking for candidates who demonstrate strength across these areas. Deep expertise in every technology listed is not expected. Candidates should not self\-select out if they do not meet all of them.
- Bachelor’s degree in a related technical field, or equivalent professional experience.
- Ten or more years delivering enterprise data or AI solutions, including substantial experience in a consulting or professional services environment.
- Expert\-level depth in one of data engineering, data visualization, or data governance, with working proficiency in at least two of the others sufficient to lead delivery and evaluate technical decisions across them.
- Demonstrated experience taking a practice capability from assessment through to an adopted standard. Examples include delivery standards, engineering or DevOps practices, accelerator libraries, interview and calibration frameworks, or competency models. Candidates should be prepared to describe what was adopted, and whether it survived after they stopped driving it.
- Experience leading technical delivery across more than one discipline simultaneously on large or complex engagements .
- Applied experience using AI to change how a delivery team works, with a clear account of what improved, how it was measured, and what did not work.
- Hands\-on delivery experience within the Microsoft data and AI ecosystem, which may include Microsoft Fabric, Azure data services, Power BI, Microsoft Purview, Copilot Studio, or Microsoft Foundry, aligned to the candidate’s area of depth.
- Excellent facilitation, presentation, and stakeholder management skills, including with executive audiences.
- Ability to work effectively through influence rather than reporting authority.
Preferred Skills and Experience
- Experience supporting presales through scoping, effort modeling, technical approach, and SOW input.
- Familiarity with DORA capabilities and their application to data and analytics delivery.
- Experience supporting regulated industry or public sector clients, including government, healthcare, financial services, or education.
- Relevant Microsoft certifications, which may include PL\- 300 , DP\-600, DP\-700 , DP\-750, GH\-300, GH\-600, or AI\-901\.
Other Requirements
- Ability to travel to SHI, partner, and customer locations and events.
- Ability to work independently while collaborating effectively with sales, delivery, engineering, and leadership stakeholders.
- Commitment to continuous learning across data, analytics, and AI platforms.
- Strong ethical standards with disciplined adherence to data privacy, governance, compliance, and security policies.
The estimated annual pay range for this position is $200,000 \- $250,000 which includes a base salary and bonus. The compensation for this position is dependent on job\-related knowledge, skills, experience, and market location and, therefore, will vary from individual to individual. Benefits may include, but are not limited to, medical, vision, dental, 401K, and flexible spending.
Equal Employment Opportunity – M/F/Disability/Protected Veteran Status
Salary Context
This $200K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SHI International, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($225K) sits 5% above the category median. Disclosed range: $200K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SHI International AI Hiring
SHI International has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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