Machine Learning Engineer

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
• Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
• Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
• Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
• Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
• Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
• Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
• Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
• Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
• Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

AI/ML Engineer

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Lead the development and implementation of advanced algorithms for robotic perception systems tailored to industrial welding tasks, integrating data from diverse vision sensors such as RGB/GigE, LiDAR, and ToF depth sensors.
• Oversee research initiatives to address complex welding-related challenges, utilizing image processing, point cloud data, and 3D sensor fusion, contributing to innovative solutions for domain-specific problems.
• Collaborate with multidisciplinary teams to design and lead experiments evaluating state-of-the-art deep learning models, optimizing machine learning systems for robotic perception in welding.
• Stay at the forefront of advancements in Robotics, Computer Vision, and ML research, driving the integration of cutting-edge technologies into real-world applications, and ensuring these innovations have a high impact on production systems.
• Mentor and guide junior engineers, providing technical leadership and fostering collaboration to enhance team expertise in perception systems and machine learning.
• Contribute to strategic decisions about system architecture and the direction of robotics perception technologies within the company, ensuring alignment with product and business goals.

Staff Machine Learning Engineer, Perception

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

• Develop and deploy AI models across the insurance value chain, leveraging both supervised and unsupervised learning techniques.
• Design, implement, and optimize Retrieval-Augmented Generation (RAG) pipelines for AI-driven decision support.
• Utilize Vector Databases and Knowledge Graphs to enhance AI applications in underwriting, claims processing, and customer engagement.
• Develop data pipelines for ingestion, transformation, and storage to support AI/ML workloads.
• Design and implement scalable solutions using cloud-based AI platforms such as Azure Machine Learning, Databricks.
• Implement MLOps best practices, including CI/CD for model training, validation, deployment, and monitoring.
• Develop generative AI models for personalized customer experiences and automation of complex decision-making processes.
• Apply natural language processing (NLP) techniques to analyze and extract insights from unstructured data sources.
• Optimize AI models for performance, scalability, and reliability in enterprise environments.
• Conduct architecture design reviews and performance tuning for AI/ML applications
• Work cross-functionally with business and technology teams to identify AI-driven opportunities and define strategies.
• Ensure compliance with ethical AI principles, model governance, and data privacy regulations.
• Demonstrate behaviors consistent with PEMCO's policies, values, code of ethics and business conduct.
• Authentically support the PEMCO Brand and constantly are on the lookout for top talent to join us to achieve our Mission to Worry Less and Live More.
• Other duties as assigned.

Senior Data and AI Scientist

• 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

• Develop actionable insights and recommendations in support of business objectives using in-house and external data sources
• Collaborate with business and data engineering professionals for timely and reliable delivery of data solutions
• Leverage deep expertise in data engineering, data science, advanced analytics, and insurance industry data to aid business partners in risk management
• Drive efficiencies and improve customer experience
• Partner with business stakeholders to scope, structure, and drive analytical projects from requirements through delivery
• Present findings and recommendations clearly to both technical and non-technical audiences
• Source, validate, and integrate structured and unstructured data across platforms
• Conduct exploratory data analysis to reveal data quality issues and identify opportunities
• Analyze business trends, time series patterns, model performance, and causal relationships using advanced statistical and machine learning / deep learning methods
• Design, build, and deploy machine learning / deep learning models — including NLP, computer vision, and LLM-based solutions
• Create visualizations, dashboards, and reports that communicate analytical and model results to support decision-making across the organization
• Own and maintain recurring analytical, reporting, and model-monitoring processes
• Document data pipelines, model architecture, analytical frameworks, and deliverables for knowledge sharing across technical and non-technical teams

Machine Learning Scientist

• Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
• Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
• Accountable for deployment design, development and maintenance of both traditional ML and AI models.
• Work with junior engineers and peers to provide mentorship and thought leadership.
• Be comfortable presenting new concepts to technical audiences.
• Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams
• Delivery of critical milestones for model deployment in the AWS and GCP clouds.
• Adopt and promote MLOps best practices to the Data Science community.

Senior AI Machine Learning 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.

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