AI Engineer

• Participate and effectively contribute to the design, development, and implementation of complex applications, often using new technologies
• Provide technical expertise and systems design for individual initiatives
• Lead the architecture and design of enterprise data, analytics, and AI solutions leveraging SAP Datasphere, SAP Analytics Cloud (SAC), and SAP BTP
• Define data architecture standards, governance models, integration strategies, and best practices
• Design scalable and secure enterprise data models supporting reporting, analytics, planning, forecasting, and AI use cases
• Develop architecture roadmaps aligned with organizational digital transformation strategies
• Architect data pipelines integrating SAP and non-SAP source systems
• Optimize data performance, scalability, and security within Datasphere environments
• Support integration between SAC, Datasphere, SAP S/4HANA, BW/4HANA, and third-party data sources
• Lead the architecture and integration of AI/ML capabilities within SAP environments
• Collaborate with data science teams to operationalize AI/ML models within enterprise workflows
• Guide development teams on APIs, data services, event processing, and cloud-native integration patterns
• Ensure enterprise security, compliance, and performance standards are met
• Develop and maintain architecture diagrams, standards, technical specifications, and operational procedures

SAP Datasphere, SAC, AI Architect

• Contribute to the architecture and take ownership of the design, development, and deployment of scalable Generative AI and Machine Learning systems into production environments.
• Develop end-to-end GenAI features, including backend API services, model integration, model monitoring, evaluations, and deployments.
• Integrate and optimize LLMs for specific business planning use cases, including prompt engineering and RAG implementation.
• Build cutting-edge conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language.
• Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics.
• Design and develop APIs that expose AI capabilities to Anaplan's platform and third-party integrations.
• Optimize model inference pipelines for performance, cost, and scalability in production environments.
• Implement monitoring, logging, and observability for GenAI systems to track usage, errors, and model behavior.
• Participate in code reviews, lead technical design discussions, and mentor junior and mid-level engineers.

Senior AI Engineer

• Building and deploying LLM-powered, production-grade systems
• End-to-end ownership across model development, optimization, and real-world integration
• Proposing & designing software solutions to address complex business needs
• Writing code, tests, and deploying software
• Preparing technical and procedural documentation required for software
• Maintaining and debugging software

Senior Software Engineer – AI Engineer

• Lead and develop a blended team of engineers (onshore and offshore), providing coaching, performance management, and career development while driving accountability for results
• Own end-to-end delivery of cloud-based and AI/ML platform initiatives, including continued development and scaling of capabilities such as AWS SageMaker and internally developed solutions
• Translate business and technology priorities into actionable delivery plans, including scope, dependencies, risks, and resource needs across a shared services model supporting multiple lines of business
• Establish and maintain strong delivery discipline through structured planning, tracking, and reporting (e.g., JIRA or similar tools) to ensure predictable and reliable outcomes
• Provide clear, data-driven communication to stakeholders and leadership on delivery status, timelines, risks, and overall progress
• Partner with engineering, product, and business teams to align on priorities, enable platform adoption, and ensure successful execution of cross-functional initiatives
• Proactively identify and mitigate risks, resolve blockers, and drive continuous improvement in delivery processes and team effectiveness
• Monitor and report on delivery performance, ensuring alignment with organizational goals and the scalability of platform capabilities
• Support operational planning, including staffing, resource allocation, and budget considerations
• Ensure adherence to technology, risk, and security standards while delivering high-quality, scalable solutions

Technology Delivery Manager – AI Platforms

• Lead the development, deployment, and ongoing optimization of AI and Generative AI solutions
• Implement engineering best practices to ensure scalability and uptime
• Track and report on platforms performance and availability metrics
• Be involved in all stages of the model development lifecycle from design to development, testing, and deployment
• Collaborate with software engineers, data scientists, and DevOps teams to design and implement technical designs and solutions
• Stay up-to-date with new technologies and industry trends

Lead AI Engineer

• Define and execute the AI architecture roadmap for Advice & Wealth Management in partnership and collaboration with other organizations
• Establish architecture patterns for both traditional ML and generative AI, including deployment and monitoring
• Help business and product leaders understand how they can leverage AI to solve their business problems
• Influence enterprise guardrails and standards for responsible AI usage (evaluation, observability, model performance, and risk management)
• Establish repeatable patterns for integrating AI into applications, workflows, and platforms
• Partner with data architecture to ensure AI-ready data foundations (quality, governance, lineage)
• Guide teams on build vs. buy vs. partner decisions for AI capabilities and platforms
• Research emerging AI technologies and patterns; translate into practical recommendations and investment proposals
• Influence senior leadership on AI strategy, investment priorities, and sequencing
• Coach and mentor architects and senior engineers in AI architecture and design practices

Chief AI Architect

• Join the Engineering team as a Forward Deployed AI Engineer
• Act as a resident AI expert within Individual Departments/Business Units
• Function as engineer, solutions architect, and internal consultant
• Design and build AI-powered application features using LLM APIs
• Create agent loops that can select tools, execute actions, and summarize results
• Develop chat-based analytical experiences connecting user questions to backend tools
• Improve prompt quality and manage context windows
• Use advanced AI coding tools to accelerate development while maintaining code quality
• Embed directly with internal departments to identify where AI can drive efficiency
• Lead the end-to-end implementation of AI solutions within operational contexts
• Provide hands-on troubleshooting and support for AI models running in production mode

AI Engineer, Forward Deployed

• Engage with the Digital and Innovation (D&I) leaders to promote the D&I vision
• Assume responsibility for the success of the enterprise data platform, data engineering delivery, and AI architecture ensuring Westinghouse digital solutions
• Lead the data engineering and AI architecture team in establishing the data architecture and data fabric
• Establish integration patterns and platform services to promote the use of industry data across multiple use cases
• Mentor team members responsible for cloud architecture and infrastructure; establish best practices for cloud platforms, security, availability, cost management, and operational support according to Westinghouse Information Technology
• Develop team member critical skills such as Data architecture, data modeling, and governance Data ingestion, transformation, and quality (ETL/ELT) Data integration, master data, and data interoperability

Manager, AI Architecture

• Design, develop, and fine-tune a variety of AI models
• Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration; employ the Model Context protocol to manage stateful interactions
• Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
• Implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
• Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning; maintain model registries, monitoring, and version control of context protocols
• Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
• Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
• Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI-safety techniques; translate new research into operational capabilities

AI Engineer – Mission Innovation Lab

• Design, build, test, and deploy custom agents, plugins, and connectors in Microsoft Copilot Studio and Azure AI Foundry
• Implement retrieval, grounding, and indexing patterns against SharePoint, Microsoft Graph
• Prototype and productionize agents that automate high-value crew workflows
• Lead proof-of-concept work for new Microsoft AI capabilities
• Partner with Workplace AI Engineer on agent governance

AI Engineer – Workplace AI

• Designing, developing, and deploying generative AI models (e.g., LLMs, diffusion models)
• Architecting and building agentic systems with autonomous decision-making capabilities
• Integrating generative AI and agentic solutions into existing products and services
• Developing and maintaining the software infrastructure required for AI/ML pipeline
• Developing and implementing data pipelines for training and evaluating AI models
• Conducting research and experimentation with new generative AI and agentic techniques
• Other duties as assigned or requested

• Design the entire ecosystem for AI solutions
• Define end-to-end architecture for AI systems, ensuring scalability and integration
• Establish best practices for MLOps
• Lead cross-functional collaboration with Data Science, DevOps, and Product teams
• Mentor senior engineers and present architectural decisions to executive leadership

Lead AI Architect

• Architect, design, and implement full-stack AI applications using .NET and modern front-end frameworks.
• Implement AI/ML models and algorithms into .NET applications to improve business processes, analytics, and user experiences.
• Provide expertise in modern front-end development, with a preference for React, to deliver seamless and scalable user interfaces.
• Work within an Agile software development framework, contributing to sprint planning, iterative product delivery, and continuous improvement.
• Focus on delivering features and solutions that provide measurable value to end-users and stakeholders, particularly in commercial product settings.
• Partner with data scientists, analysts, and other developers to deliver secure, efficient solutions.
• Write clean, maintainable, and testable code following modern design principles and best practices.
• Ability to articulate architecture and design recommendations to leadership and contribute to the firm’s AI strategy.
• Stay current on AI advancements, .NET releases, and front-end frameworks to keep our technology stack modern and effective.
• Maintain technical documentation, versioning, and knowledge transfer materials.

Senior Full Stack AI Engineer

• Own the end-to-end administration of the Association’s primary AI platform and Microsoft Copilot
• Manage user provisioning and deprovisioning, SSO configuration, permission tiers, and seat allocation across standard and Pro/Max license tiers
• Ensure platform configurations meet enterprise security standards and Responsible AI guidelines
• Design, build, and maintain integrations between AI platforms and core enterprise systems
• Partner with Enterprise Risk to ensure all AI platform configurations meet data security policies
• Track platform consumption patterns and generate usage analytics
• Serve as the primary technical point of contact with AI platform vendors
• Lead technical due diligence when new AI tools are proposed
• Own the build and maintenance of internal CoE tooling
• Forecast platform capacity needs as adoption grows

Director of AI Platform Operations

• Design and implement AI-powered customer support solutions (chatbots, virtual agents, agent assist tools)
• Build and maintain systems for automating ticket triage, classification, routing, and response generation
• Develop retrieval-augmented generation (RAG) systems using internal knowledge bases and documentation
• Integrate AI into support platforms (e.g., Salesforce Service Cloud, etc.)
• Analyze workflow data (tickets, chats, call transcripts) to identify automation opportunities and insights
• Optimize AI systems for accuracy, latency, cost, and customer satisfaction
• Establish evaluation frameworks (e.g., response quality, resolution rate, deflection rate)
• Collaborate with services and support teams to continuously refine workflows and ensure practical usability
• Implement guardrails and monitoring to ensure safe, reliable, and compliant AI behavior

AI Developer – Customer Services and Support

• Build and evolve AI automations and agent-based workflows
• Design scalable agent architecture and orchestration patterns
• Suggest and implement AI-driven automation enhancements
• Act as an internal AI advocate by providing training and mentorship
• Partner with senior engineers and cross-functional teams
• Participate in the full AI SDLC, including design sessions

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Own the end-to-end product quality of every user-facing application the AI Platform ships
• Build the frontend architecture and the shared design system / component library used across every AI Platform application
• Ensure RBAC-aware interfaces, making sure program teams, governance stakeholders, leadership, and platform engineers see the right data and controls
• Lead the backend-for-frontend layer, API patterns, data contracts with MLOps, and the seams where the UI meets the platform
• Define the application-level testing strategy: unit, integration, and end-to-end suites
• Instrument every application for adoption, feature engagement, error rates, and load times, and surface insights that inform the platform roadmap
• Optimize frontend performance, including bundle optimization and lazy loading
• Define and enforce UX quality gates that no application ships below
• Collaborate with application teams to build user-facing applications alongside MLOps engineers
• Wire telemetry into every surface and read the generated data for informed decision making

Senior Full Stack Engineer, AI Platform

• Embed directly with clients to gather requirements, translating ambiguous operational challenges into concrete technical architectures and delivering high-impact, AI-driven solutions with extreme ownership.
• Train and deploy custom AI agents to prevent fraud and automate complex fincrime workflows across a diverse portfolio of use cases.
• Utilize advanced prompt engineering, fine-tuning, and rigorous evaluation frameworks to continuously optimize AI agent performance, ensuring high accuracy, safety, and reliability.
• Champion an AI-first approach across internal teams and client engagements, demonstrating the tangible business impact of advanced machine learning interventions.
• Establish and evolve our AI risk metrics, the supporting datasets, and the evaluation pipelines used to measure the causal impact of deployed agents.
• Collaborate tightly with internal engineering and product teams to scale custom models into production, ensuring seamless integration and optimized operational performance.

Forward Deployed AI Engineer

• As a technical lead, you will own the architecture and technical roadmap for object perception systems used by the robot in production.
• Design, develop, and deploy machine learning algorithms for multi-object detection, scene understanding, and 6-DoF object pose estimation.
• Evaluate and drive adoption of state-of-the-art perception models.
• Promote best practices in architecture, design, and testing to deliver high-quality, scalable software.
• Optimize deep neural networks and associated data processing to run efficiently on embedded systems.
• Collaborate with navigation, manipulation and hardware teams to align perception capabilities with product requirements.

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SAP Datasphere, SAC, AI Architect

Senior AI Engineer

Senior Software Engineer – AI Engineer

Technology Delivery Manager – AI Platforms

Lead AI Engineer

Chief AI Architect

AI Engineer, Forward Deployed

Manager, AI Architecture

AI Engineer – Mission Innovation Lab

AI Engineer – Workplace AI

AI Engineer

Lead AI Architect

Senior Full Stack AI Engineer

Director of AI Platform Operations

AI Developer – Customer Services and Support

Senior AI Engineer

AI Engineer

Senior Full Stack Engineer, AI Platform

Forward Deployed AI Engineer

Staff AI Engineer, Perception