AI Engineer

• Support the design, development, and deployment of AI-driven, data-centric solutions in strategic client environments
• Collaborate with senior client stakeholders and technical teams on AI and data strategy initiatives
• Support the full solution lifecycle from problem definition and architecture design through prototyping, deployment, scaling, and adoption
• Align client technology roadmaps with business objectives and emerging AI trends
• Develop and implement AI and LLM-powered applications using RAG, autonomous agents, and orchestration frameworks
• Rapidly develop functional prototypes and production-ready demos
• Support technical expansion opportunities and account growth
• Contribute to proposals, technical demos, and client engagements
• Articulate AI/ML capabilities and business value to technical and non-technical stakeholders
• Mentor and guide junior engineers and data scientists
• Contribute to EY’s Forward Deployed Engineering frameworks, best practices, and technical capabilities

Forward Deployed AI Engineer – Associate Director

• Design, build, and launch new AI-focused engineering capabilities and modules
• Own engineering initiatives end to end, from early-stage solutioning and architecture through implementation and launch
• Remain deeply hands-on with software development and contribute production code
• Build solutions using Python and apply strong distributed-systems engineering principles
• Participate in system design, architecture, and solutioning discussions with engineering teams
• Work closely with the Google Gemini team to bring NextIQ capabilities into Gemini
• Collaborate with external technology partners to extend Nextiva capabilities into their environments
• Partner with U.S.-based Nextiva product and engineering teams on new initiatives
• Work with teams supporting NextOS to bring NextIQ capabilities into other areas of the Nextiva ecosystem
• Navigate ambiguous, early-stage technical problems and translate them into practical engineering solutions
• Take accountability for designing, building, and successfully launching owned capabilities
• Apply AI-assisted software engineering practices and contribute to AI-related initiatives involving NextIQ and partner environments

Staff AI Engineer

• Design, develop, and optimize complex Azure SQL databases
• Lead database projects and troubleshoot performance issues
• Build and maintain autonomous, multi-agent AI systems that reason, plan, execute tasks, and collaborate with systems and humans
• Perform prompt engineering and build RAG pipelines
• Maintain and support Microsoft Azure App Services and C# .NET applications
• Perform data warehousing and ETL
• Mentor junior developers
• Write technical documentation

Apps Developer – AI Agents, SQL, .NET

• Design, build, and ship LLM-powered and agentic product features that change how customers manage their finances
• Build agentic AI systems that reason over context, invoke tools, take real actions, and recover gracefully from failure
• Architect and implement production-grade RAG pipelines over sensitive financial data, with strict requirements for correctness, auditability, and safety
• Contribute to shared AI infrastructure, including LLM services, agent orchestration frameworks, and evaluation and monitoring tooling
• Own production systems, including reliability, latency, cost, and failure modes
• Work closely with product and design partners, focusing on customer outcomes
• Operate in a modern, enterprise-scale environment with a strong emphasis on agentic AI
• Use Python, Go, TypeScript, AWS and/or GCP, Kubernetes, REST, gRPC, Kafka, LLMs, agent orchestration, retrieval-augmented generation, vector storage, evaluation, schema validation, and state management

Senior AI Engineer – Enterprise Architecture

• Design, develop, enhance, and streamline secure and scalable AI and Agentic AI solutions for the Arizona Department of Economic Security
• Work with the internal AI product management team, vendor teams, and development staff
• Translate business and technical requirements into production-ready AI solutions
• Create technical specifications, architecture, and design documentation
• Exercise independent judgment in AI solution architecture and implementation

AI Architect

• Supervise and mentor AI Engineers and cross-functional colleagues on tasks and projects
• Integrate AI models into production systems in collaboration with cross-functional teams
• Monitor and ensure compliance with model governance, security, and compliance standards
• Guide and support junior AI engineers
• Design and develop AI/machine learning frameworks for large-scale data processing, prompt engineering, and generative AI
• Apply machine learning algorithms to prompt engineering and natural language processing problems
• Translate complex business requirements into AI/machine-learning-driven solutions
• Build and optimize Big Data pipelines and AI-based systems to automate business processes
• Implement natural language processing models to classify and analyze complex business text data
• Establish machine learning operations pipelines for scalable model deployment, monitoring, and maintenance
• Design and evolve distributed AI systems integrating models, LLMs, and data services
• Develop multi-stage retrieval pipelines, autonomous workflows, orchestration frameworks, and system-level evaluation
• Establish standards for AI reliability, safety, explainability, observability, and regulatory compliance
• Collaborate with product and business teams to align technology initiatives with business objectives
• Manage complex tasks, influence technical areas across multiple teams and projects, and make critical decisions affecting project success and operational efficiency

Senior Agentic AI Engineer II – Global Infrastructure

• Develop and optimize AI/ML models for integration with production environments
• Build and enhance scalable, efficient data pipelines for AI processing
• Participate in designing and reviewing AI architectures aligned with enterprise standards
• Implement model monitoring and evaluation techniques to improve system performance
• Ensure AI solutions meet governance, security, and compliance requirements
• Support and troubleshoot AI models and data processing pipelines in production
• Collaborate with product and business teams to align technology initiatives with business objectives
• Develop and integrate production AI system components, including ML models and LLM-based services
• Contribute to model training pipelines, inference services, and retrieval workflows
• Apply enterprise standards for observability, safety, and compliance

AI Engineer – Global Infrastructure

• Define end-to-end architectures for enterprise AI and ML solutions spanning machine learning, Generative AI, agentic AI, data, applications, APIs, platforms, infrastructure, and enterprise systems
• Translate business opportunities and requirements into architecture blueprints, technical strategies, success criteria, and implementation roadmaps
• Determine technical approaches for complex business problems, including software, machine learning, Generative AI, retrieval-augmented generation, and agentic AI
• Develop reusable reference architectures, design patterns, standards, technical guardrails, and enterprise AI capabilities
• Architect AI solutions involving model selection and routing, retrieval and grounding, context engineering, agent orchestration, tool use, state and memory, human-in-the-loop workflows, evaluation, observability, and secure enterprise integration
• Define architecture patterns for AI/ML data pipelines, model serving, model lifecycle management, MLOps, deployment, monitoring, and operations across cloud, hybrid, on-premises, and restricted environments
• Evaluate technology and platform alternatives and lead build, buy, configure, and integrate decisions
• Lead architecture and technical design reviews
• Partner with enterprise architecture, cybersecurity, data, identity, privacy, Responsible AI, business, product, engineering, and technology teams
• Provide technical leadership and mentorship across engineering teams

• Design, develop, and deploy production-grade AI and ML solutions using traditional machine learning, Generative AI, retrieval-augmented generation, agentic AI, and software engineering
• Build AI agents and intelligent workflows that reason, use tools, interact with enterprise applications and data, and execute complex multi-step processes with human oversight
• Develop retrieval and context-engineering solutions using enterprise data, embeddings, vector and enterprise search, knowledge sources, prompts, and memory
• Integrate AI solutions with enterprise applications, APIs, data sources, and tools using standard interfaces and Model Context Protocol (MCP)
• Evaluate and select models and solution approaches based on quality, reliability, latency, cost, security, scalability, and business requirements
• Develop systematic evaluation cases to measure solution performance
• Develop production-quality software, APIs, integrations, tools, and reusable AI components for end-to-end AI solutions
• Diagnose and improve AI system behavior using evaluations, traces, telemetry, user feedback, and failure analysis
• Address groundedness, task completion, robustness, and production reliability issues
• Partner with AI Architecture, Platform Engineering, Data, Evaluation, Cybersecurity, and business teams to move solutions into secure, scalable production environments

Applied AI Engineer

• Build and iterate on Generative AI applications, including retrieval-augmented generation (RAG) systems and lightweight agent workflows
• Develop data pipelines that bring together logs, metrics, and operational data to support AI-driven use cases
• Prototype quickly and help transition successful ideas into production-ready tools
• Apply LLMs, embeddings, and vector databases to solve problems in incident management and system operations
• Collaborate with engineers and product partners to identify high-impact opportunities for AI in production environments
• Contribute to deployment, monitoring, and ongoing improvement of AI-powered systems
• Design and scale AI systems in real-world production environments
• Build reliable, observable systems with measurable effectiveness and outcomes
• Work across teams to turn ideas into adopted mainstream solutions