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

Machine Learning Engineer

• Provides leadership in hiring, coaching, talent development, performance management, and compensation decisions
• Partners with Enterprise, Solution, and Domain Architects to define AI/ML solution architectures
• Leads cross-functional delivery across Product, Data Science, Platform, and Engineering teams
• Establishes engineering practices, reusable frameworks, and platform capabilities for AI/ML initiatives
• Oversees the design, implementation, and evolution of data, feature, and model pipelines
• Applies expertise in machine learning, statistics, and optimization methodologies
• Evaluates data quality and feature readiness in partnership with Data Science teams
• Drives operational excellence through automation and continuous improvement practices
• Engages business and technology stakeholders to understand objectives and assess opportunities
• Communicates technical strategy and delivery progress to senior leaders
• Participates in special projects and performs other duties as assigned

Manager, Machine Learning Engineer

AI Engineer

• Design, build, and evolve cloud-native platform services and AI/GenAI capabilities that improve developer productivity and software delivery outcomes.
• Architect and deliver enterprise-grade AI solutions, including RAG and GraphRAG pipelines, multi-agent systems, LLM-powered developer tools, plugins, MCP tools, and reusable skills.
• Drive Claude Suite adoption end-to-end, including claude.ai, Claude API, Claude Code, system prompt design, and context window management.
• Lead large-scale Cursor AI integration, including workspace rules, .cursorrules governance, MCP server connections, and AI-assisted development workflows.
• Build and operate LLM evaluation pipelines using RAGAS, LangSmith, and custom evaluation harnesses.

AVP DevX – AI Engineer, Technical Lead

• 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

• 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 the strategic vision and roadmap for AI architecture practice across the enterprise, identifying high-impact opportunities for AI adoption
• Lead the design and development of robust, scalable, and secure AI architectures, and integration with existing IT infrastructure
• Serve as the top technical leader for AI architecture, guiding the selection of appropriate platforms, frameworks, and cloud technologies
• Build, lead, and mentor a multidisciplinary team of Architects fostering a culture of innovation and best practices
• Collaborate with architects from various functional domains, executive leadership, engineers, security specialists, and business stakeholders to provide direction for AI solutions and articulate complex technical concepts effectively to non-technical audiences
• Stay abreast of emerging AI advancements, such as Generative AI, agentic workflows, and drive innovation through prototyping and validating new technologies.

SVP, Head of AI Architecture

• 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

• Champion Agile principles and practices across AI and data-focused teams, driving adoption and continuous improvement
• Coach product owners, engineers, and business stakeholders on Agile frameworks such as Scrum, Kanban, or SAFe
• Facilitate core Agile ceremonies (sprint planning, stand-ups, reviews, retrospectives) to ensure effective team execution
• Foster a culture of transparency, collaboration, and iterative delivery across cross-functional teams
• Partner with technical teams (data science, ML engineering) and business leaders to translate AI use cases into actionable backlogs and sprint goals
• Guide teams in delivering AI/ML solutions iteratively, accounting for experimentation, model training, and validation cycles
• Align delivery plans with business objectives, ensuring AI initiatives provide measurable value
• Support prioritization of work based on business impact, technical dependencies, and data readiness
• Enable collaboration across diverse teams, including data scientists, engineers, product managers, and domain experts
• Remove impediments and escalate risks impacting delivery timelines or outcomes
• Act as a liaison between technical and non-technical stakeholders to ensure shared understanding
• Define and track Agile and AI-specific performance metrics (e.g., velocity, cycle time, model performance indicators)
• Ensure AI delivery adheres to governance, compliance, and ethical standards (e.g., bias, data privacy)
• Facilitate retrospectives and apply learnings to improve delivery effectiveness and team maturity
• Promote data-driven decision-making and experimentation
• Support enterprise Agile transformation efforts, particularly within AI and digital initiatives
• Influence leaders and stakeholders to adopt Agile ways of working and AI-enabled delivery models
• Drive mindset shifts toward iterative development, experimentation, and continuous learning

AI Platform Agilist

• Partner deeply with the business organization to understand their data, workflows, operator behavior, and where AI can drive measurable improvement in throughput, quality, and customer outcomes.
• Architect and lead the development of foundational and agentic AI capabilities for the partner domain—applying modern modeling techniques, retrieval-augmented architectures, and agentic patterns at enterprise scale.
• Design long-running and multi-step agents that reason over rich enterprise context to deliver measurable business outcomes within a regulated environment.
• Build for production from day one —model evaluation harnesses, drift detection, monitoring, and deterministic deployment patterns that meet the bank's Model Risk Management and AI governance bar.
• Operate across the data and modeling application boundary —deploying models into the modeling environment while consuming data and context from the regulated data platform, in close coordination with the platform team.
• Influence architecture across two organizations —serving as the senior AI voice in the partner team's technical forums and as the domain voice in Cognitive AI Solutions architectural discussions.
• Mentor and uplevel engineers in both organizations on agentic AI, foundational modeling, and applied AI engineering practices.
• Translate technical progress into executive narrative —supporting demos and presentations to senior leadership and partner stakeholders.

Principal Applied AI Engineer

• 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

• Design and build multi-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A
• Lead end-to-end delivery of agentic underwriting and claims automations, from prototype through production hardening
• Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable
• Lead offshore engineers and team members on agentic patterns, prompt engineering, and reliability practices
• Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence
• Translate business intent into working agentic systems — not just systems that compile, but systems that deliver measurable business outcomes.

AVP, AI Engineering

• Convert research into real products (not just slide decks or notebooks)
• Help build workflows that diversify datasets and/or populate data
• Ship machine learning workflows/pipelines fast and iterate faster
• Leverage LLM/VLM and agents in the data generation pipeline
• Define evaluation criteria and run offline evals before any model or prompt change reaches production

Senior Systems Software Engineer, Machine Learning

• Guide the architecture, design, and development of our Cloud platforms and automations
• Design and implement highly scalable, multi-threaded, and distributed applications
• Design and implement AI Agents to monitor and optimize cloud resources
• Integrate AI-driven insights with Cloud Service Providers
• Ensure robust performance, reliability, and security for cloud-based deployments
• Conduct code reviews and offer constructive feedback
• Continuously optimize processes to improve developer productivity
• Develop predictive models for drift detection, cost anomaly detection, and forecasting
• Automate operational workflows using machine learning
• Design, build, and maintain scalable ETL pipelines
• Use AWS Athena for interactive querying of data
• Manage and optimize data storage and processing using Snowflake
• Orchestrate complex workflows and data pipelines using Apache Airflow DAGs
• Continuously evaluate emerging AI technologies and tools

AI/ML Engineer

• Build scalable, robust ingestion pipelines for processing clickstream data, handling both participant-level (full-day employee activity) and case-level (end-to-end case lifecycle) data streams.
• Design and implement streaming platform-based orchestration for pipeline coordination, ensuring reliable data flow and processing guarantees.
• Deploy and manage containerized services on enterprise container platforms, implementing CI/CD pipelines and infrastructure-as-code practices.
• Implement comprehensive observability for LLM and VLM pipelines, including: Performance monitoring and metrics collection, Distributed tracing for multi-model pipelines, Logging and alerting for model inference, Cost tracking and optimization.
• Build fault-tolerant systems with retry mechanisms, circuit breakers, dead letter queues, and graceful degradation patterns.
• Work with VLMs to process clickstream video data and generate high-quality transcripts for downstream analysis.
• Ensure seamless integration with downstream Analysis & Evaluation workstream.

Lead 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

• Design, build, and iterate on AI Assistants integrated with enterprise tools including Jira, Slack, and internal data sources
• Develop and maintain prompt engineering frameworks, evaluation pipelines, and feedback loops to continuously improve assistant quality and reliability
• Build and maintain APIs, backend services, and integrations that connect AI capabilities to the tools teams already use
• Collaborate with AI Ops, security, and platform engineering teams to ensure systems are production-grade, secure, and observable
• Contribute to a repeatable framework for building new AI Assistants as the program expands
• Evaluate new models, tools, and libraries and make informed recommendations on when and how to adopt them
• Identify and mitigate risks related to AI outputs, data handling, and responsible use
• Contribute to technical documentation, runbooks, and onboarding materials that help the team scale what gets built

Staff AI Engineer

• 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

• Support the Product Manager in refining and executing the agentic AI digital media platform strategy
• Own end-to-end delivery of AI-powered features from concept to production
• Design new intuitive user experiences powered by intelligent automation
• Partner with engineering to define AI system behavior and interaction patterns
• Translate new business workflows into scalable product functionality
• Improve evaluation metrics for AI-driven features (accuracy, reliability, latency, ROI impact)
• Lead experimentation to improve AI-driven feature performance
• Improve human-in-the-loop review and escalation workflows
• Collaborate with engineering to implement APIs and integrations
• Support stakeholder analysis to understand user behavior and requirements
• Improve user testing programs to validate assumptions
• Contribute to product roadmap definition and own detailed roadmap execution and prioritization
• Translate business requirements into product specifications and user stories
• Manage backlog, sprint planning, and delivery cadence
• Prioritize initiatives based on ROI and technical feasibility
• Collaborate with sales, marketing, and customer success for launches
• Monitor product performance and drive continuous improvement

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