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About This Role
Role Overview: As an Applied AI Engineer III , you will actively engage in your engineering craft, taking a hands\-on approach to building and enhancing high\-visibility, full\-stack products that serve the business and its users. Your expertise will be pivotal in delighting customers and users, while driving tangible value across Deloitte's product and AI investments. You will leverage your extensive engineering craftsmanship across full\-stack software engineering and modern frameworks\-together with applied AI fluency that lets you build GenAI and agentic capabilities directly into the products you deliver\-consistently demonstrating your strong track record in delivering high\-quality, outcome\-focused solutions. The ideal candidate will be a dependable team player, collaborating with cross\-functional teams to design, build, and ship products end to end, from concept through production.
Key Responsibilities:
- Outcome\-Driven Accountability: Embrace and drive a culture of accountability for customer and business outcomes\-and for the cost of achieving them. Develop engineering solutions that solve complex problems with valuable outcomes, ensuring high\-quality, lean designs and implementations, and owning the inference, token, and cloud cost of what you build.
- Technical Leadership and Advocacy: Serve as the technical advocate for products, ensuring code integrity, feasibility, and alignment with business and customer goals. Lead requirement analysis, component design, development, testing, integrations, and support.
- Engineering Craftsmanship: Maintain accountability for code\-design integrity, implementation fidelity to architecture and tech stack, quality, data, and ongoing maintenance and operations. Be hands\-on, self\-driven, and continuously learn new approaches, languages, and frameworks. Create technical specifications, and write high\-quality, supportable, scalable code ensuring all quality KPIs are met or exceeded. Demonstrate collaborative skills to work effectively with diverse teams.
- Customer\-Centric Engineering: Develop lean engineering solutions through rapid, inexpensive experimentation to solve customer needs. Engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
- Incremental and Iterative Delivery: Adopt a mindset that favors action and evidence over extensive planning. Utilize a leaning\-forward approach to navigate complexity and uncertainty, delivering lean, supportable, and maintainable solutions.
- Cross\-Functional Collaboration and Integration: Work collaboratively with empowered, cross\-functional teams including product management, experience, and delivery. Integrate diverse perspectives to make well\-informed decisions that balance feasibility, viability, usability, and value. Foster a collaborative environment that enhances team synergy and innovation.
- Advanced Technical Proficiency: Possess expertise in modern software engineering practices and principles, including AI and Agentic SSDLC to deliver daily product deployments using full automation from discovery to production to operations with all quality checks through SSDLC lifecycle. Strive to be a role model, leveraging these techniques to optimize solutioning and product delivery. Demonstrate strong understanding of the full lifecycle product development, focusing on continuous improvement and learning.
- Domain Expertise: Quickly acquire domain\-specific knowledge relevant to the business or product. Translate business/user needs, architectures, and UX/UI designs into technical specifications and code. Be a valuable, flexible, and dedicated team member, supportive of teammates, and focused on quality and tech debt payoff.
- Effective Communication and Influence: Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence teammates and product teams through well\-structured arguments and trade\-offs supported by evidence. Create coherent narratives that align technical solutions with business objectives.
- Engagement and Collaborative Co\-Creation: Engage and collaborate with product engineering teams at all organizational levels, including customers as needed. Build and maintain constructive relationships, fostering a culture of co\-creation and shared momentum towards achieving product goals. Align diverse perspectives and drive consensus to create feasible solutions.
The team:
US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost\-effective model that focuses on value/outcomes that leverages a progressive and responsive talent structure. As Deloitte's primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom\-line results and outcomes. It helps power Deloitte's success. It is the engine that drives Deloitte, serving many of the world's largest, most respected companies. We develop and deploy cutting\-edge internal and go\-to\-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.
The successful candidate will possess:
- Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.
Qualifications:
Required:
- A bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline. Experience is the most relevant factor.
- 5\+ years of experience with most of the following: Angular, React, NodeJS, Python, C\#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, as well as unit testing frameworks.
- 3\+ years of experience building AI/ML and agentic applications, with hands\-on GenAI experience across LLM integration (OpenAI, Anthropic, or open\-source models), RAG pipelines, prompt engineering, vector databases, evaluations, and AI agent orchestration.
- 3\+ years of experience with cloud\-native engineering, using FaaS, PaaS, or micro\-services on any of the cloud hyperscalers such as Azure, AWS, or GCP, including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI, plus application\-level infrastructure\-as\-code and cost\-aware engineering (FinOps accountability).
- Prior software engineering experience with the understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations, and AI\-augmented spec\-driven development.
- Prior experience using methodologies \& tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (e.g. LangFuse, LangSmith, or equivalent multi\-agent orchestration tools) etc. to deliver high\-quality products rapidly.
- Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
- Limited immigration sponsorship may be available.
- Candidates must be located within a commutable distance to one of the select locations available for this role
- Ability to work in your local office at a minimum of 3 days per week
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 $102,500 \- $210,600\.
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.
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Salary Context
This $102K-$210K range is below the median for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Deloitte, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($156K) sits 28% below the category median. Disclosed range: $102K to $210K.
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
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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 Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI 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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