AI Engineer

• Designing and building agentic AI systems including LLM orchestration, RAG pipelines, and multi-agent frameworks
• Leading, mentoring, and growing AI Engineers
• Conducting one-on-one meetings, performance reviews, and identifying career development opportunities
• Establishing engineering practices, coding standards, and technical processes for AI systems

Lead 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

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Build and nurture meaningful relationships with clients
• Utilize advanced analytical techniques to drive innovation
• Work with cross-functional teams to achieve project goals
• Uphold the firm's ethical standards and recommended practices

Senior AI Engineer

• Manage client accounts and secure the delivery of exceptional results
• Foster sturdy relationships with clients to understand their needs
• Analyze complex data sets to derive actionable insights
• Confirm adherence to quality standards and established practices in deliverables

AI Engineer Manager

• 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

• Serve as the engineering lead for Agentic AI delivery across Supply Planning and Manufacturing — owning the design, development, and deployment of production-grade AI agent solutions.
• Architect and build multi-agent AI systems using Azure AI Agent Service, AutoGen, Semantic Kernel, and/or LangChain/LangGraph — including orchestrator-executor patterns, tool calling, memory management, and agent coordination.
• Implement the MCP to surface enterprise data as structured context for AI agents operating in supply chain and manufacturing workflows.
• Build and deploy generative AI solutions on Azure AI Foundry — RAG-based knowledge agents, decision support for forecasting and capacity planning, and document intelligence for maintenance work orders and recipes.
• Design and deliver AI copilots and topic-based agents using Microsoft Copilot Studio — enabling Supply Planning and Manufacturing teams to access insights and take action directly from Teams and Outlook.
• Act as the AI delivery owner for agentic use cases — scoping business problems with stakeholders, defining agent capabilities and tool surfaces, prioritizing the roadmap, and driving adoption.
• Apply emerging agentic AI patterns — including ReAct, Plan-and-Execute, reflection, and human-in-the-loop — for supply chain and operational use cases.
• Partner with Supply Chain leadership, Demand Planning, Process Engineering, Maintenance Ops, and Plant teams to identify, scope, and deliver AI use cases that influence operational decisions.
• Define and maintain AI agent governance — prompt versioning, tool auditing, evaluation frameworks, observability, and safety guardrails for production deployments.
• Develop on Azure Databricks — PySpark and SQL against gold/platinum Delta tables, notebooks for transformation and feature work, and orchestration via Workflows.
• Build and maintain Power BI reports and semantic models that serve as grounding data for AI agents and executive dashboards across Supply Planning and Manufacturing.
• Own Supply Chain AI metrics alignment cadence — keeping priorities, status, and roadblocks visible to Supply Chain and Manufacturing leadership.
• Mentor analysts and engineers on agentic AI design patterns, MCP, and AI delivery best practices.

Lead Agentic AI Engineer

• analyze complex problems and provide strategic insights
• mentor and guide junior associates in their roles
• maintain rigorous standards of quality and compliance in deliverables
• navigate complex situations to achieve project goals
• support enterprise-scale transformation initiatives for clients

Senior Associate – AI Engineering

• Lead the data engineering team (3 engineers, 2 analysts); conduct performance reviews, provide mentorship, and set career development plans to drive growth
• Work cross-functionally with CS, Sales, Marketing, and Finance to identify needs and construct technical roadmaps for data initiatives
• Establish and enforce enterprise-wide governance frameworks that ensure data privacy, AI safety, and regulatory compliance while standardizing best practices across the data ecosystem
• Oversee the design and deployment of a unified and scalable data architecture and pipelines that support analytics, BI, and machine learning initiatives
• Evaluate business friction points to determine if a BI dashboard, AI agent, or automated workflow is the most effective solution
• Define and track ROI via dashboards for "Hours Saved," "Cost Avoidance," and "Revenue Impact" from AI and data deployments

Director of Data, AI Engineering

• 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

• 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, 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.

• 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

• Creates and drives the strategy for business across artificial intelligence and machine learning systems that address diverse business challenges throughout the Bank.
• Leads a team of AI Engineers who conduct rigorous data analysis, performing statistical evaluation, designing experimental frameworks, and developing algorithms that effectively leverage both structured and unstructured data.
• Develops and implements AI and machine learning systems that address specific business challenges and deliver measurable value.
• Creates and maintains model documentation, ensuring transparency in methodologies and approaches.
• Researches, tests, and applies state-of-the-art generative AI models and/or solutions for potential use.
• Develops agentic AI systems capable of autonomous reasoning, planning, and tool utilization for complex task completion.
• Creates comprehensive evaluation frameworks to assess model performance, detect hallucinations, and ensure output quality.
• Contributes to establishing best practices for AI development and deployment.
• Develops and executes the AI portfolio strategy, overseeing budgets, vendor relationships, and talent planning to ensure alignment with business objectives and regulatory requirements.
• Owns strategy and results for the AI function, interpreting organizational needs, recommending best practices, and ensuring alignment with business objectives and compliance requirements.
• Extracts meaningful patterns and insights from complex, multi-dimensional data sets.
• Applies advanced analytics including predictive modeling, machine learning, and optimization techniques.
• Translates business questions into well-defined analytical problems with clear objectives.
• Designs and executes experiments with statistically valid methodologies and evaluation criteria.
• Develops specialized analytics for banking-specific use cases while maintaining compliance with financial regulations.
• Manages large projects or teams, providing direct supervision to three or more professionals and overseeing area planning, prioritization, and execution.
• Partners with cross-functional teams to understand business requirements and translate them into technical solutions.
• Effectively communicates complex technical concepts to non-technical stakeholders.
• Establishes and governs risk management, compliance frameworks, and value realization metrics for all AI initiatives, reporting outcomes to senior leadership and driving continuous improvement.
• Presents findings, recommendations, and insights to business teams in accessible formats.
• Collaborates with software engineers, cloud engineers, data engineers, and data scientists to integrate AI solutions into existing systems and products.
• Works with compliance and security teams to ensure AI systems meet banking regulatory requirements.

Director, AI Engineering

• 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.

• 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

• Support the Digital Workplace by building, managing, monitoring, and governing AI-enabled platforms and automation solutions
• Partner with product owners, governance teams, and business stakeholders to design, deploy, and operate AI agents, workflow automations, and AI-assisted applications
• Conduct regular monitoring of infrastructure platforms and systems to identify performance issues or anomalies
• Analyze collected data, establish performance baselines, and create reports to identify trends and potential areas of improvement
• Collaborate with System and Solution Architects, Senior Platform Engineers, Governance Regulatory and Compliance, as well as Product Owners to implement performance tuning strategies
• Monitor digital workplace platforms including Power Platform, Copilot Studio agents, and supporting services to identify performance, reliability, usage, and cost trends
• Document support requests, resolutions, and troubleshooting steps
• Provide technical support to users, troubleshoot and resolve complex hardware and software issues
• Assist in the design & execution of disaster/system recovery and testing processes and standards
• Develop and implement reporting frameworks to track and analyze platform and system performance and utilization metrics

See all jobs on JobTailor

Lead AI Engineer

AI Engineer – Architect, IFS Cloud

Senior Agentic AI Architect

QuantAI Engineer – Trading, Investment & Optimization

Senior AI Engineer

AI Engineer Manager

AI Engineer – Advisor Software

Senior Agentic AI Architect – Engineer

Lead Agentic AI Engineer

Senior Associate – AI Engineering

Director of Data, AI Engineering

Senior Software AI Engineer

Director of AI Platform Operations

AI Engineer

AI Engineer

Director, AI Engineering

Senior AI Engineer, Agentic Evaluation – V&V

AI Engineer – Architect, IFS Cloud

Forward Deployed AI Engineer

Digital Workplace – AI Platforms Engineer