Design, build, test, and deploy machine learning and artificial intelligence solutions for business and client use cases; develop and maintain data pipelines, model training workflows, and production-grade application components; analyze structured and unstructured data; collaborate with engineers, data scientists, product stakeholders, and business teams; monitor model and application performance, troubleshoot issues, and implement enhancements.
Compensation
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 $110,700 to $218,300. 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.
Responsibilities
Design, build, test, and deploy machine learning and artificial intelligence solutions for business and client use cases
Develop and maintain data pipelines, model training workflows, and production-grade application components that support AI-enabled products
Analyze structured and unstructured data to identify patterns, generate insights, and support model development and validation
Collaborate with engineers, data scientists, product stakeholders, and business teams to translate requirements into technical solutions
Monitor model and application performance, troubleshoot issues, and implement enhancements to improve accuracy, reliability, and scalability
Requirements
Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a quantitative field
Nice to have: Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a quantitative field
Nice to have: US government security clearance
Nice to have: experience deploying machine learning models into production environments
Nice to have: experience working with large language models, natural language processing, or generative artificial intelligence solutions
Help design, build and continuously improve the clients online platform. Research, suggest and implement new technology solutions following best practices/standards. Take responsibility for the resiliency and availability of different products.
Responsibilities
Lead globally distributed engineering teams
Drive end-to-end ML lifecycle management
Define technical strategy and roadmap for ML sustenance across multiple services
Seeking a forward-thinking professional with an AI-first mindset to design, develop, and deploy enterprise-grade solutions using Generative and Agentic AI frameworks that drive innovation, efficiency, and business transformation.
Responsibilities
Lead AI-driven solution design and delivery by applying GenAI and Agentic AI to address complex business challenges
Automate processes
Integrate intelligent insights into enterprise workflows for measurable impact
Lead AI-driven solution design and delivery by applying GenAI and Agentic AI to address complex business challenges, automate processes, and integrate intelligent insights into enterprise workflows for measurable impact.
Responsibilities
Lead AI-driven solution design and delivery
Apply GenAI and Agentic AI to address complex business challenges
Automate processes
Integrate intelligent insights into enterprise workflows
Help shape how AI is built securely at scale. In this role, you’ll combine AI security architecture with hands‑on engineering to protect emerging AI and agentic systems from development through deployment.
Compensation
Salary Range:
Kč948,290.00 - Kč1,761,110.00
Responsibilities
Shape secure AI architectures, standards and patterns across enterprise AI platforms and solutions.
Define security strategies protecting AI models, datasets, pipelines and supporting infrastructure.
Assess AI environments, identify vulnerabilities and mitigate risks throughout the AI lifecycle.
Design secure agentic AI workflows, Model Context Protocol servers and integrations.
Embed security into AI development, deployment and Machine Learning Operations processes.
Design and scale data pipelines and machine learning infrastructure for an AI-first platform, using AI tools to multiply output and working with product/ML engineers.
Compensation
The base salary is $170,000 to $200,000 for US-based candidates, plus equity.
Responsibilities
Design and build reliable data pipelines that process large volumes of customer warehouse data
Build and run the machine learning operations infrastructure that takes models from training to production: feature pipelines, training jobs, deployment, and monitoring
Own data quality and data modeling to ensure accurate and trustworthy data
Use AI coding tools effectively by writing clear specs and context, running agents on parallel workstreams, and reviewing their output critically
Build shared AI workflows, guardrails, and review standards
The Senior Artificial Intelligence (AI) Architect provides enterprise level architecture leadership for the agency’s adoption of AI, shaping strategy and delivering secure, scalable and responsible solutions aligned with business priorities.
Senior AI Architect providing enterprise-level architecture leadership for AI adoption within the Australian Government.
Compensation
$140 - $160 per hour (inc. super)
Responsibilities
Develop and maintain AI architecture strategy, target architecture, principles, standards, reference architectures, reusable patterns and roadmaps
Translate business, policy, service delivery and ICT requirements into secure, scalable, resilient and supportable end-to-end AI architectures
Assess AI opportunities, platforms, models and tools against strategic alignment, business and public value, feasibility, data readiness, interoperability, cost, risk, performance, supportability and vendor dependency
Define and assure architecture patterns, guardrails and controls across the AI lifecycle for generative AI, large language models, retrieval-augmented generation, machine learning, intelligent automation and emerging AI technologies
Provide architecture assurance and clear advice on options, trade-offs and risks to senior stakeholders and governance forums
Requirements
Relevant tertiary qualifications or recognised certifications in architecture, information technology, artificial intelligence, machine learning, computer science, data science, engineering, cloud or cyber security
Nice to have: Experience delivering AI architecture within the Australian Government or another large, complex and highly regulated environment
Lead design and implementation of agentic analytics systems and data pipelines for Google Payments, collaborating with product, data science, and engineering teams to deliver AI-enabled insights and governance.
Responsibilities
Design and build agentic analytics systems leveraging LLMs and AI
Translate product needs into production-grade data systems (models, pipelines, services)
Lead design and coordination of analytics infrastructure powering product analytics across payments
Build AI-native analytics workflows embedding intelligence directly into data and decision systems
Partner with product, data science, and engineering to shape problem definitions and deliver solutions
Requirements
Bachelor’s degree or equivalent practical experience
Develop and maintain the agency’s AI architecture strategy, target architecture, principles, standards, reference architectures, reusable patterns and roadmaps.
Translate business, policy, service delivery and ICT requirements into secure, scalable, resilient and supportable end-to-end AI architectures.
Assess AI opportunities, platforms, models and tools against strategic alignment, business and public value, feasibility, data readiness, interoperability, cost, risk, performance, supportability and vendor dependency.
Define and assure architecture patterns, guardrails and controls across the AI lifecycle for generative AI, large language models, retrieval-augmented generation, machine learning, intelligent automation and emerging AI technologies.
Provide architecture assurance and clear advice on options, trade-offs and risks to senior stakeholders and governance forums.
Requirements
Relevant tertiary qualifications or recognised certifications in architecture, information technology, artificial intelligence, machine learning, computer science, data science, engineering, cloud or cyber security
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