AI Engineer

• Define and own end-to-end enterprise architecture for a Microsoft-based customer engagement and CCaaS platform
• Establish target-state architecture across sales, service, channels, integrations, data, and security
• Develop architecture principles, standards, patterns, and governance guardrails
• Architect solutions across Dynamics 365 Sales, Customer Service, and Omnichannel
• Design end-to-end customer and seller journeys including Lead-to-opportunity, Opportunity-to-close, Account management, Case management and resolution
• Define architecture for: Sales automation, Case management and service workflows, Routing, queues, workstreams, and skills-based distribution
• Architect solutions leveraging Power Platform, Microsoft Teams and Microsoft 365
• Define patterns for integration with third-party and ISV platforms
• Define AI governance including prompt design, security, and responsible AI controls
• Organize and oversee architecture authority across programs and workstreams.

Dynamics 365 Enterprise AI Architect

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

AI Research Scientist

• 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

• 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

Machine Learning 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

• Design, develop, and maintain both front-end interfaces and backend services with AI capabilities
• Create and manage RESTful APIs and integrate AI services such as LLMs, embeddings, and document parsing tools
• Extract, transform, and analyze large datasets to enhance product features and user experience
• Implement highly optimized machine learning pipelines with quantized models for legacy hardware
• Ensure high code quality through rigorous testing, code reviews, and debugging
• Proactively solve technical issues and contribute to all phases of the development lifecycle
• Stay updated with emerging technologies and incorporate them into our operations where beneficial

Senior Agentic AI Engineer

• design and build AI‑powered solutions using Microsoft’s AI ecosystem, primarily Azure, GitHub, Copilot Studio, and Azure AI Foundry
• develop and integrate conversational copilots, retrieval‑augmented generation (RAG) experiences, and automation workflows into enterprise applications
• collaborate closely with product, engineering, security, and business stakeholders to deliver scalable, compliant, and measurable AI capabilities that support utility and energy customers
• Build and maintain AI solutions leveraging Azure AI Foundry, including model selection, prompt orchestration, evaluation, and deployment patterns, with a focus on reliable and customer‑centric outcomes
• develop and configure copilots using Copilot Studio, including topics, actions, plugins/connectors, and inclusive conversation design
• Implement AI integration patterns such as retrieval‑augmented generation (RAG), tool/function calling, and agent‑based workflows into applications and internal tooling
• create and maintain API services (for example, Azure Functions, App Service, and containerized services) to expose AI capabilities to enterprise business systems
• design and implement secure and accountable data access patterns for enterprise knowledge sources, including SharePoint, Azure SQL, and Blob Storage
• Use Azure DevOps and GitHub for source control, pull requests, code reviews, branching strategies, and release management in a collaborative and agile environment
• Build CI/CD pipelines that support automated testing, security scanning, and deployments to Azure
• Apply engineering best practices, including unit and integration testing, telemetry, error handling, performance tuning, and clear documentation
• Follow secure‑by‑design practices, including identity and access management (Entra ID), secrets management (Key Vault), and least‑privilege access
• Support responsible AI and governance requirements, including logging, auditing, evaluation, content safety, and prompt and security guardrails
• partner with security and compliance teams to ensure AI solutions align with enterprise policies and regulatory requirements common in the utilities and energy sector
• translate business needs into AI solution designs, user stories, and technical delivery plans that are practical and measurable
• work cross‑functionally with product, engineering, and business partners to define success metrics, validate outcomes, and iterate based on feedback and analytics
• provide guidance and enablement to teams adopting Copilot Studio and Azure AI Foundry patterns, fostering shared ownership and continuous learning

AI Developer

• 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

• Lead End‑to‑End Solution Delivery: Own delivery end‑to‑end: requirements → architecture → design → build → test → deployment
• Serve as the technical lead: set engineering standards, unblock delivery, and ensure quality across testing and release
• Design and implement AI solutions using Azure AI services (e.g., Azure OpenAI, Cognitive Services, Azure Machine Learning) and, where appropriate, Copilot / Copilot Studio
• Emphasize security, governance, and real‑world usability over experimentation for its own sake
• Build data platforms using Azure data services (ADF, Databricks, Synapse, ADLS) and/or Microsoft Fabric
• Deliver production‑grade pipelines (ETL/ELT), storage and compute patterns, and data models supporting BI and AI workloads
• Use Power Platform (Power Apps, Power Automate, Power BI) when it’s the right tool for rapid business value
• Partner with engineers and business stakeholders to translate requirements into buildable designs
• Coordinate with security and governance teams to ensure solutions meet best practices and client constraints
• Communicate architecture decisions — including tradeoffs — in plain language
• Act as a trusted advisor: align scope to outcomes, manage expectations, and keep clients informed when things change
• Create reusable internal assets (reference architectures, accelerators, playbooks) that improve delivery quality
• Stay current on Azure AI and data platform changes and apply learning pragmatically to client solutions
• Support early discovery, solution envisioning, and contribution to technical proposals, estimates, and SOWs as needed.

Azure Data & AI Architect

NLP Engineer

• Provide support for a large-scale, multi-site Electronic Health Record (EHR) system deployment.
• Assist with planning, execution, and coordination of assigned workstreams, ensuring alignment with project goals, timelines, and budgets.
• Report to your workstream lead, collaborating across technical teams, clinical stakeholders, site leadership, and senior management, where necessary.
• Support all phases of deployment, including pre-deployment, go-live, and post-deployment optimization.
• Communicate routinely with key stakeholders to provide updates, address challenges, and maintain momentum on deliverables.
• Cultivate trusted relationships across organizations (client, vendor, and internally) to ensure seamless management of deployment activities.
• Develop deep knowledge of client's practices, policies, and processes to effectively carryout activities within the role’s purview.
• Analyze and process large free-text datasets (primarily response to open ended questions in training and change management surveys) to develop efficient methods for clients to review and interpret feedback and sentiments, identify concerns, recognize positive trends, and pinpoint areas for improvement to inform actionable decisions.

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