Interested in this AI Software Engineer role at Accenture?
Apply Now →Skills & Technologies
About This Role
We are:
A forward\-thinking services company at the forefront of AI\-native innovation. We partner with enterprise clients to create next\-generation, agent\-powered workflows engineered to scale in real\-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.
You are
An AI Native Engineer with a strong foundation in building cloud\-native solutions and hands\-on experience designing and deploying agentic systems, especially for enterprise environments. You’re a critical thinker who thrives in ambiguity, delivering concrete results by designing, building, and running AI agents that augment workflows and scale across modern infrastructure.
You'll shape how enterprises adopt AI\-native engineering \- either by leading complex agentic solutions and developing engineering talent, or by owning critical technical areas end\-to\-end as a senior IC
The Work
You’ll partner directly with client stakeholders — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be net\-new platforms and systems that need to be stitched together in our clients’ environments alongside our ecosystem partners.
Agent Architecture \& Engineering
- Design and build enterprise\-ready AI agents incorporating retrieval, orchestration, policy\-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
- Implement resilient, testable, and maintainable agentic workflows that can be iterated on quickly.
AI Platform Integration
- Develop and/or extend abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and multi\-provider enablement.
- Contribute to shared libraries, SDKs, and patterns that can be reused across clients.
Cloud\-Native Engineering
- Leverage containerization (Kubernetes, Docker), microservices, serverless, event\-driven architectures, CI/CD, and observability stacks to deliver scalable AI\-native systems.
- Own deployment, monitoring, and troubleshooting for your services in production.
Domain\-Specific Workflows
- Tailor and deploy agentic applications across verticals (e.g., finance, healthcare, retail), adapting to domain\-specific processes and constraints.
- Work closely with client SMEs to translate business workflows into agentic solutions.
Client Engagement
- Participate in and/or lead design workshops, POCs, and code\-with sessions to shape data\-driven agent workflows with stakeholders, fostering trust and adoption.
- Communicate trade\-offs, risks, and recommendations clearly to both technical and non\-technical audiences.
Measure \& Improve
- Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
- Iterate rapidly based on data, feedback, and changing requirements.
Knowledge Sharing
- Craft reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.
- Contribute to internal communities of practice around AI\-native and agentic engineering.
Travel may be required for this role. The amount of travel will vary from 25% to 75% depending on business need and client requirements.
Here’s What You Need
- Minimum of 3 years of engineering experience with cloud\-native systems (APIs, microservices, containerization, serverless).
- Minimum of 1 year of hands\-on experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production or near\-production environments.
- Minimum of 1 year of experience with modern AI platforms — OpenAI, Claude, Vertex AI, or open\-source models — including building or using abstraction layers for multi\-provider pipelines.
- Minimum of 3 years strong Python, Java or equivalent experience building 12 factor applications \+ Infrastructure as Code (Terraform, Helm)
- Minimum of 3 years of experience in client\-facing communication and collaboration, including leading technical discussions, workshops, or delivery sessions under ambiguity.
- Bachelor's degree in Computer Science, Engineering or equivalent OR equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
Bonus Points If You Have:
- Relevant AI certifications or agentic tooling experience are a plus.
- You’ve served as an Agentic / AI Engineer in an enterprise environment.
- You’ve built multi agent orchestrations using (Lang\-graph, Crew AI, Claude SDK, Open AI SDK, etc).
- Have a GitHub repo with an agent/plugins you have created
- You have additional AI certifications or experience with agentic tooling and frameworks.
- You’ve defined or worked with enterprise\-grade architectures for compound AI systems, orchestration frameworks, or agent registry / stream\-based architectures.
- You understand the AI\-native paradigm — blending cloud\-native with generative model architectures — optimizing for performance, modularity, and efficiency.
- You’ve delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs.
- Driven execution across multiple workstreams, ensuring quality, delivery, and alignment with client outcomes.
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 05/11/2026\.
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long\-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
U.S. Employee Benefits \| Accenture
Role Location Annual Salary Range
California $70,350 to $235,100
Cleveland $59,100 to $188,100
Colorado $63,800 to $203,100
District of Columbia $68,000 to $216,300
Illinois $59,100 to $203,100
Maryland $63,800 to $203,100
Massachusetts $63,800 to $216,300
Minnesota $63,800 to $203,100
New York $66,300 to $235,100
New Jersey $68,000 to $235,100
Washington $80,200 to $216,300
Requesting an Accommodation
Accenture is committed to providing equal employment opportunities for persons with disabilities or religious observances, including reasonable accommodation when needed. If you are hired by Accenture and require accommodation to perform the essential functions of your role, you will be asked to participate in our reasonable accommodation process. Accommodations made to facilitate the recruiting process are not a guarantee of future or continued accommodations once hired.
If you would like to be considered for employment opportunities with Accenture and have accommodation needs such as for a disability or religious observance, please call us toll free at 1 (877\) 889\-9009 or send us an email or speak with your recruiter.
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
For details, view a copy of the Accenture Equal Opportunity Statement
Accenture is an EEO and Affirmative Action Employer of Veterans/Individuals with Disabilities.
Accenture is committed to providing veteran employment opportunities to our service men and women.
Other Employment Statements
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.
Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process. Further, at Accenture a criminal conviction history is not an absolute bar to employment.
The Company will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Additionally, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the Company's legal duty to furnish information.
California requires additional notifications for applicants and employees. If you are a California resident, live in or plan to work from Los Angeles County upon being hired for this position, please click here for additional important information.
Please read Accenture’s Recruiting and Hiring Statement for more information on how we process your data during the Recruiting and Hiring process.
Salary Context
This $63K-$235K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $189K across 518 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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At Accenture, 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 $235,100 based on 665 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($149K) sits 36% below the category median. Disclosed range: $63K to $235K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Accenture AI Hiring
Accenture has 872 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Consultant. Positions span Morristown, NJ, US, New York, NY, US, Arlington, VA, US. Compensation range: $68K - $434K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above the national median.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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 (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.