AI Engineer

• Lead the end‑to‑end application architecture for IFS Cloud across Projects, FSM, Resource Management, Manufacturing and Finance.
• Define and govern IFS Cloud reference architectures, design patterns, and guardrails, aligned with Evergreen and SaaS operating principles.
• Promote a configuration‑first mindset, minimizing customization and ensuring long‑term maintainability.
• Translate business and operational requirements into scalable, secure, and supportable IFS Cloud solutions.
• Define environment strategies across DEV, TEST, and PROD, supporting secure delivery and operational stability.
• Design extension strategies using standard IFS capabilities (APIs, Business Events, projections, external services).
• Ensure non‑functional requirements, performance, availability, security, and resilience are built into solution designs.
• Architect and enable AI use cases, including embedded IFS AI offerings: Service planning and scheduling optimization, Demand forecasting and anomaly detection
• Design AI‑augmented business processes, promoting AI insights directly into operational workflows.
• Ensure people trust AI recommendations through human-in-the-loop decisioning. Define feedback loops to monitor outcomes and improve AI‑enabled capabilities.
• Architect end-to-end integration solutions between IFS Cloud and upstream CRM platforms, such as lead-to-order, service demand, and contract lifecycle.
• Design event‑triggered integration architectures using IFS Business Events as the primary mechanism for integrations.
• Ensure systems integrate through the common model rather than point‑to‑point mappings, improving scalability and maintainability.
• Promote loose coupling between IFS Cloud and external systems to support Evergreen upgrades and platform evolution.
• Support enterprise reporting, and advanced analytics by ensuring data accuracy and lineage. Collaborate with analytics and data platform teams to align IFS data structures with enterprise BI, AI, and machine learning initiatives.

AI Engineer – Architect, IFS Cloud

• Architect AI Agents that use Tools and Skills including instantiation of MCP clients and servers.
• Architect scalable end-to-end Retrieval-Augmented Generation (RAG) pipelines including data ingestion, document processing, and promoting generation.
• Increase model serving layers and high-throughput data processing pipelines, employing advanced libraries to ensure ultra-low latency and efficient resource use.
• Design and implement secure system architectures for all new AI products ensuring compliance with data governance and security policies.
• Understand how to use Terraform for provisioning and manage Azure infrastructure-as-code.
• Support automated and reproducible Continuous Integration/Continuous Deployment (CI/CD) pipelines.

Senior Agentic AI Architect

• Set up the technical infrastructure required for AI systems, including hardware, software, and data storage solutions.
• Design and configure computing environments to support the execution of AI models.
• Optimize AI models and algorithms for efficient execution and scalability.
• Automate AI infrastructures for the data science team.
• Leverage techniques such as model pruning, quantization, and parallel computing to reduce computational requirements and improve performance.
• Integrate AI systems into existing software frameworks or platforms, ensuring compatibility and seamless interaction with other components.
• Define APIs and protocols for communication between AI systems and external services.
• Implement and maintain CI/CD (Continuous Integration and Continuous Delivery) pipelines to automate the deployment, testing, and monitoring of AI systems.
• Ensure rapid and reliable software releases.
• Monitor the performance and health of AI systems, detecting and addressing issues, such as system failures, performance degradation, or data inconsistencies.
• Apply debugging and troubleshooting techniques to resolve technical problems.

AI Engineer – Advisor Software

• Implement our enterprise AI vision by architecting Generative AI and Agentic AI solutions
• Architect AI Agents that use Tools and Skills including instantiation of MCP clients and servers
• Architect scalable end-to-end Retrieval-Augmented Generation (RAG) pipelines, including data ingestion and document processing
• Increase model serving layers and high-throughput data processing pipelines
• Design and implement secure system architectures for all new AI products
• Understand how to use Terraform for provisioning and manage Azure infrastructure-as-code.

Senior Agentic AI Architect – Engineer

• Architect AI Agents that use Tools and Skills including instantiation of MCP clients and servers
• Architect scalable end-to-end Retrieval-Augmented Generation (RAG) pipelines
• Increase model serving layers and high-throughput data processing pipelines
• Designing and implementing secure system architectures for all new AI products
• Understand how to use Terraform for provisioning and manage Azure infrastructure-as-code
• Understand centralized observability to support automated and reproducible CI/CD pipelines

• Lead in the execution of the strategic direction for ML, NLP, LLM, and algorithm development within a world-class AI ML team, working closely with well-renowned experts in AI/ML modeling, ML engineering, data science, and data engineering
• Partner with AI leadership to identify opportunities to leverage AI technologies to improve efficiency, productivity, and innovation
• Collaborate closely with business stakeholders, data scientists, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems
• Partner on the execution of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM model development and ensure the end-to-end solution meets all technical and business requirements, and SLA specifications
• Complete the development and deployment of AI models and algorithms to solve complex problems
• Ensure the robustness, scalability, and performance of AI systems and solutions
• Stay updated on advancements in AI research and technology to guide initiatives
• Foster a culture of innovation, collaboration, and continuous learning within the team
• Coach, support, and provide mentorship for team members
• Ensure stakeholder needs are met through effective communication and collaboration
• Promote understanding of AI capabilities and limitations across the organization
• Champion ethical AI practices and ensure compliance with regulatory standards
• Address issues related to data privacy, security, and biases in AI systems
• Develop policies that promote transparency and accountability in AI applications.

Principal AI Engineer

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Design, develop, and deploy GenAI and LLM‑based solutions, including RAG pipelines, prompt orchestration, agent workflows, and supporting Python services.
• Select appropriate models, tools, and frameworks based on quality, performance, cost, security, and enterprise requirements.
• Own AI solutions end‑to‑end, from design and development through production deployment and operational support.
• Define solution architectures aligned with Azure AI platform standards, enterprise guardrails, and ISC data governance guidelines.
• Establish and apply best practices for prompt design, testing, evaluation, versioning, reliability, scalability, and maintainability.
• Implement automated build, test, deployment, monitoring, and feedback processes in Microsoft‑centric enterprise environments.
• Coordinate user acceptance testing, collect structured feedback, and incorporate operational signals to continuously improve solution quality and adoption.
• Ensure compliance with Rockwell AI policies, risk controls, and data handling standards throughout development and deployment.
• Collaborate with ISC AI COE leadership, stakeholders, and cross‑functional teams to align on use cases, priorities, and technical approaches.
• Partner with business users to validate solution behavior, identify improvements, and enable effective use of GenAI‑powered tools and workflows.

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• 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

• Manages and leads a program involving one or more functions and project teams to drive the engineering development and implementation process for a subsystem or component of a product or service offering.
• Develops schedules, critical deliverables, budget, resource allocation plan, and other support requirements for assigned program.
• Coordinates activities of supporting project teams and internal and external development partners; tracks progress against established plan and makes decisions to alter or update schedule and resource allocation to meet product requirements and development schedule.
• Communicates program progress, escalations, and issue analysis to product stakeholders; collaborates with management and internal manufacturing and development partners to implement changes to product, process, or program plan to resolve escalated issues, produce solutions, and ensure adherence to budgets and established product roadmaps and schedules.
• Leads and provides guidance and mentoring to less-experienced staff members.

HPC & AI Engineering Program Manager III

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• Lead the end‑to‑end application architecture for IFS Cloud across Projects, FSM, Resource Management, Manufacturing and Finance.
• Define and govern IFS Cloud reference architectures, design patterns, and guardrails, aligned with Evergreen and SaaS operating principles.
• Promote a configuration‑first mindset, minimizing customization and ensuring long‑term maintainability.
• Translate business and operational requirements into scalable, secure, and supportable IFS Cloud solutions.
• Define environment strategies across DEV, TEST, and PROD, supporting secure delivery and operational stability.
• Design extension strategies using standard IFS capabilities (APIs, Business Events, projections, external services).
• Ensure non‑functional requirements, performance, availability, security, and resilience are built into solution designs.
• Architect and enable AI use cases, including embedded IFS AI offerings: Service planning and scheduling optimization, Demand forecasting and anomaly detection.
• Collaborate with analytics and data platform teams to align IFS data structures with enterprise BI, AI, and machine learning initiatives.

• Manages and leads a program involving one or more functions and project teams to drive the engineering development and implementation process
• Develops schedules, critical deliverables, budget, resource allocation plan, and other support requirements for assigned program
• Coordinates activities of supporting project teams and internal and external development partners
• Communicates program progress, escalations, and issue analysis to product stakeholders
• Leads and provides guidance and mentoring to less-experienced staff members.

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