AI Infrastructure Architect
About the job
- A Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field is required.
- A minimum of 2 years of experience coding, building, monitoring or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines or cloud/platform engineering solutions is required.
- Strong understanding of AI/ML concepts and the compute, storage, networking, security and deployment foundations required to run AI workloads is required.
- A minimum of 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash or PowerShell is required.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration and operational monitoring tools is required.
- Hands-on experience with AWS services relevant to AI infrastructure such as Amazon EC2, Amazon EKS, Amazon SageMaker, Amazon S3, AWS Identity and Access Management, Amazon VPC and Amazon CloudWatch is required.
- Experience designing or operating GPU/accelerated compute, distributed training setups, containerized deployments and model-serving workloads is required.
- Working knowledge of Terraform or CloudFormation, Docker, Kubernetes, CI/CD pipelines and observability practices is required.
- Ability to optimize infrastructure for performance, reliability, scalability, cost and security is required.
- Understanding of MLOps patterns including experiment tracking, model registry, model deployment, monitoring and rollback approaches is required.
- Strong problem-solving ability, communication skills and collaboration in a fast-paced engineering environment are required.
- Architect and build custom artificial intelligence infrastructure and hardware solutions.
- Optimize artificial intelligence infrastructure and hardware performance, power consumption, cost and scalability of the computational stack.
- Advise on artificial intelligence infrastructure technology and vendor evaluation, selection and full-stack integration.
- Design, build, automate, monitor and optimize artificial intelligence and machine-learning infrastructure on Amazon Web Services for reliable, scalable and cost-effective model development and production workloads.
- Write, review and debug code, scripts and infrastructure-as-code for Amazon Web Services artificial intelligence infrastructure, automation, monitoring and deployment tooling.
- Configure and provision Amazon Web Services compute resources for artificial intelligence and machine-learning workloads, including GPU-enabled instances, Amazon EC2, Amazon EKS, Amazon SageMaker and supporting storage and networking services.
- Support deployment automation and continuous integration and continuous delivery pipelines for artificial intelligence systems, models and applications using Git, Terraform or CloudFormation, Docker, Kubernetes and workflow orchestration tooling.
- Deploy and operate artificial intelligence services, model-serving components and data pipelines while applying reliability, security, cost-efficiency and scalability practices.
- Monitor infrastructure and model-serving health using Amazon CloudWatch and related observability tools, and troubleshoot issues across compute, storage, networking, containers and application layers.
- Collaborate with data scientists, machine-learning engineers, platform engineers and architects to integrate artificial intelligence models into enterprise systems while meeting compliance and operational requirements.
- Document reusable patterns, configuration standards and runbooks for Amazon Web Services-based artificial intelligence infrastructure.
- An AWS certification such as AWS Certified Solutions Architect, AWS Certified Developer, AWS Certified DevOps Engineer or AWS Certified Machine Learning Specialty or Associate is preferred.
- Exposure to industry use cases in banking, financial services and insurance, healthcare, retail or e-commerce, telecommunications, manufacturing or the public sector where artificial intelligence infrastructure must meet compliance, reliability and data-governance expectations is preferred.
- Familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling or model optimization techniques is preferred.
- Knowledge of security controls, FinOps practices, incident management and production support processes for enterprise artificial intelligence platforms is preferred.
About the company
Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.
Company Size
10,001+
Company Stage
IPO
Headquarters
Dublin, Ireland
Founded
1989
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Simplify's Take
What believers are saying
- Business demand grew: October 2026 results showed $74.2 billion annual revenue, up 5% in local currency.
- Eligible US employees buy discounted stock: Accenture's purchase plan sells shares at 15% below market price.
- Client relationships run deep: Accenture has worked with 195 of its top 200 customers for more than ten years.
What critics are saying
- Bookings are not guaranteed revenue: most Accenture contracts let clients cancel quickly with little or no penalty.
- Promotions require AI adoption: Accenture's CEO said in March 2026 that employees must use its tools effectively.
- Federal hiring practices proved costly: Accenture settled discrimination allegations for $25 million in September 2026, without a liability finding.
What makes Accenture unique
- Its AI platform switches models: AI Refinery selects among prebuilt models according to cost, performance and other requirements.
- Accenture owns specialist Microsoft expertise: it cofounded the Microsoft-focused Avanade consultancy with the software maker.
- Consulting includes creative agencies: Accenture Song owns businesses spanning advertising, digital product design and customer engagement.
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Benefits
Health Insurance
Professional Development Budget
401(k) Retirement Plan
401(k) Company Match
Company News
Accenture (NYSE: ACN) is betting on enterprise AI as its main growth engine. At its Investor and Analyst Day, the company targeted at least mid-single-digit constant-currency revenue growth in fiscal 2027 and 2028, rising to mid- to high-single-digit growth from fiscal 2029. Chief executive officer Julie Sweet said AI will create new demand that exceeds work lost to automation. The company has grown its AI and data workforce from 40,000 to nearly 110,000. Accenture plans to return $9.5 billion to shareholders in fiscal 2027 and spend about $5 billion on acquisitions. It expects annual operating-margin growth of 10 to 30 basis points. Its network of specialised businesses, now 6% of revenue, could double by fiscal 2029. Fiscal 2026 revenue was $74.2 billion, up 5% in local currency.
Accenture shares rose 10.4% after the company reported higher sales and earnings for its fourth quarter and full year in early October 2026. It confirmed fiscal 2027 guidance, raised its quarterly dividend to $1.71 per share, and completed a long-running share repurchase program. The company also expanded its AI alliances with Dell and Workday and acquired Sagacity. It aims to embed agentic AI into client functions such as HR, finance, and customer engagement. Accenture's narrative projects $86.3 billion in revenue and $10.3 billion in earnings by 2029, up from $8.4 billion today. Pricing pressure and slower discretionary spending remain the main risks.
Accenture will hold an Investor Day on October 14, where it is expected to detail its long-term strategy, AI monetisation plans, growth guidance, and margin expansion goals. The event follows a rebound in its shares after fears that AI would erode traditional consulting work. The stock hit a low of $118.15 in June, down from a January high of $291.09. It has since gained nearly 77%, helped by fourth-quarter results on October 1 and an expanded collaboration with Dell Technologies. Fourth-quarter revenue rose 6% to $18.7 billion, and new bookings reached $22.2 billion. Accenture expects fiscal 2027 revenue growth of 3% to 6% in local currency. It raised its quarterly dividend by 5% to $1.71 per share, payable November 13, 2026. Its market capitalisation is about $139.6 billion.
Accenture has acquired Sagacity, a London-based data and customer intelligence firm, to strengthen Accenture Song's AI-powered customer services. The deal brings more than 140 professionals to Accenture Song's UK and Ireland operations. Terms were not disclosed. Founded in 2005 by Anita Dougall, Malka Townshend, and Harry Dougall, Sagacity offers data assets, AI-enabled technology, and customer intelligence expertise. Accenture says clients want to connect data, AI, and operations into unified programmes that deliver measurable results. The move targets industries with large customer bases, such as utilities, retail, banking, and telecommunications. "Sagacity's deep expertise and outcome-focused approach will enhance our ability to help clients turn insight into action," said Sohel Aziz, senior managing director of Accenture Song, UK and Ireland.
TM Forum has launched a multi-year Trustworthy AI programme with Accenture to help telecoms deploy autonomous operations with verifiable trust standards. The initiative will deliver the industry's first Trustworthy AI Governance Certification, providing a benchmark for trusted AI deployment. Recent TM Forum research found that whilst 72% of communications service providers believe their AI capabilities are trustworthy, only 14% can verify their claims. The programme aims to transform AI trust from aspiration into measurable operational capability. The certification will help organisations assess governance maturity, workforce readiness, and operational accountability needed to scale AI autonomously. It will operate through TM Forum's AI-Native Collaboration Studios, where telecoms operators, vendors, and partners will contribute real-world use cases to shape the framework.