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About This Role
Google AI Architect/AI and Engineering
Join our AI \& Engineering team in transforming technology platforms, driving innovation, and helping make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and re\-engineering 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.
AI \& Engineering leverages cutting\-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission\-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology \& data platforms. Our delivery models are tailored to meet each client's unique requirements.
Engineering as a Service provides complete design, implementation, and technology operations, leveraging our core engineering expertise. We transform engineering teams, modernize technology, and deliver complex programs with a product engineering approach. Our flexible delivery models\-traditional teams, pools, or pods\-are tailored to each client's needs, offering engineering\-led advisory, implementation, and operational capabilities to accelerate innovation.
Recruiting for this role ends on 10\-31\-2026
Work you'll do:
- Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini; optimize for scalability, reliability, security, and cost.
- Design, fine\-tune, evaluate, and govern LLM solutions with Gemini on Vertex AI (prompt/tool/function calling, safety policies, Vector Search, evaluation); implement deployment, inference optimization, and monitoring.
- Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability.
- Define end\-to\-end architectures across data pipelines, feature engineering, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build.
- Lead cloud\-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce application and agentic design patterns.
- Implement security and governance for AI/ML systems (data privacy, model poisoning, adversarial attacks); apply Gemini safety features and enterprise guardrails.
Responsibilities include:
- Architect and Design: Design and development of enterprise\-grade AI applications and platforms, with a focus on scaling AI solutions for production. This includes defining the technical architecture, selecting appropriate technologies, and ensuring solutions are robust, scalable, and secure.
- LLM and AI Integration: Integrate and fine\-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an emphasis on production\-level performance and reliability.
- Enterprise Architecture: Collaborate with enterprise architects to ensure AI solutions align with the broader company's technical strategy, governance, and standards.
- Cloud and GenAI Native Development: Design and deploy applications using Cloud Native principles on a hyperscaler platform (AWS, Azure, GCP). Leverage a wide range of hyperscaler tools and services, including containers (Docker, Kubernetes), serverless functions, and managed databases. Should have experience in leveraging various GenAI tools to accelerate software development life cycle.
- Security \& Governance: Ensure the security of all AI/ML systems by addressing potential vulnerabilities such as data privacy concerns, model poisoning, and adversarial attacks.
- Design Patterns: Apply and enforce Application Design Patterns and Agentic Design Patterns to build resilient and maintainable software systems.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering or a related technical field.
- 6\+ years' experience as a Software or Solution Architect, with a strong focus on application development and scaling solutions for production environments.
- 5\+ years hands\-on with Google Cloud, including 2\+ end\-to\-end enterprise implementations in production.
- 4\+ years designing and implementing Google Cloud networks, security controls, and landing zones using Terraform.
- 2\+ years building and operating containerized workloads on GKE (autoscaling, ingress, monitoring/observability).
- 2\+ years implementing CI/CD and DevSecOps with Cloud Build, GitHub Actions, or Jenkins.
- 3\+ years executing migration or modernization programs to Google Cloud (rehost, replatform, refactor).
- 2\+ years applying AI/GenAI on Google Cloud with Vertex AI and Gemini, including 1\+ years' production deployment (e.g. RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability).
- Deep understanding of AI/ML concepts, including experience with LLMs and their application in enterprise settings.
- Experience implementing multiple AI solutions in a professional, real\-world environment.
- Strong understanding of security implications related to AI/ML systems (e.g., data privacy, model poisoning, adversarial attacks).
- Familiarity with various hyperscaler tools and services.
- Hyperscaler Architect certification is required (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert, or GCP Professional Cloud Architect).
- Ability to travel up to 50% based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available.
Preferred Qualifications:
- Google Professional Machine Learning Engineer certification or the equivalent ML certification.
- Master's degree in technology\-related discipline.
- 2\+ years's leading high performance, results driven engineering teams delivering AI platforms or applications.
- 1\+ year implementing LLMOps/MLOps using Vertex AI Pipelines and Cloud Build (or similar)
Wages \+ Salary
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 $122,000\-$240,500\.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
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 $122K-$240K range is below the median for AI Architect roles in our dataset (median: $181K across 29 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Deloitte, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($181K) sits 24% below the category median. Disclosed range: $122K to $240K.
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.
Deloitte AI Hiring
Deloitte has 59 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Research Engineer. Positions span Rosslyn, VA, US, Baltimore, MD, US, Morristown, NJ, US. Compensation range: $140K - $379K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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