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

• Design, develop, and deploy AI/ML and Generative AI solutions that address business and operational challenges at enterprise scale.
• Develops and maintains infrastructure systems that connect internal data sets; creates new data collection frameworks for structured and unstructured data.
• Develop reusable AI capabilities including RAG pipelines, vector search, semantic retrieval, prompt orchestration, and agentic workflows.
• Implement evaluation frameworks and automated testing strategies to measure model quality, accuracy, bias, safety, and performance.
• Establish monitoring, observability, and governance processes to ensure AI systems remain reliable and compliant in production.
• Drive adoption of Responsible AI practices by implementing evaluation standards, audit-ready documentation, and model governance controls.
• Optimize AI systems for scalability, latency, reliability, and cost efficiency.

AI Machine Learning Scientist

• Manage one or more Applications Development teams in an effort to accomplish established goals as well as conduct personnel duties for team (e.g. performance evaluations, hiring and disciplinary actions)
• Utilize in-depth knowledge and skills across multiple Applications Development areas to provide technical oversight across systems and applications
• Review and analyze proposed technical solutions for projects
• Lead the design, development, and implementation of sophisticated AI/ML, Generative AI, and Agentic AI solutions, including AI assistant tools, in close collaboration with AI architects, product owners, and cross-functional engineering teams within the Engineering Excellence framework
• Architect and develop end-to-end AI systems, leveraging both Python for machine learning workflows and Java for scalable enterprise-grade backend services, ensuring seamless integration with existing and new applications
• Design and implement intelligent agentic systems that exhibit autonomous decision-making capabilities, enabling advanced automation and self-optimizing processes
• Develop, fine-tune, and optimize Large Language Models (LLMs) using both parameter-efficient techniques and full fine-tuning, focusing on their integration into robust, production-ready systems supported by both Python and Java components
• Drive the implementation and experimentation with advanced generative AI methods such as prompt engineering and Retrieval-Augmented Generation (RAG), ensuring their effective deployment and performance in enterprise environments
• Own the deployment pipeline for AI models and associated services, leveraging CI/CD methodologies, containerization (Docker), and orchestration (Kubernetes) on leading cloud platforms (AWS, Azure, GCP) to ensure secure, scalable, and automated releases
• Apply deep knowledge of database technologies (SQL and NoSQL) to design efficient data storage, retrieval, and management strategies for AI applications, ensuring data integrity and performance
• Contribute to the establishment and enforcement of engineering best practices, architectural patterns, and tooling standards for full-stack AI development, advocating for maintainability, observability, and cost-efficiency
• Stay abreast of the latest advancements in AI/ML, Agentic AI, cloud technologies, and DevOps trends, proactively sharing knowledge and driving adoption of innovative solutions
• Ensure strict adherence to ethical AI guidelines, data privacy regulations, and compliance standards throughout the entire AI solution lifecycle
• Act as a mentor for junior engineers, providing expert guidance on Python, Java, cloud technologies, and CI/CD best practices, fostering a culture of technical excellence and continuous improvement.

Senior AI/ML Engineer – Full Stack Developer

AI Engineer

• Design AI solutions for customer experience use cases, including self-service, agent assistance, journey management, and back-office automation.
• Support solution designs that combine LLMs, deterministic workflows, tools, orchestration layers, fallback strategies, and human handoff patterns.
• Support retrieval-augmented generation, knowledge, and content strategies that help ground AI responses in trusted enterprise data.
• Assist with approaches for content structure, chunking, retrieval, relevance, and freshness while balancing performance, latency, and governance considerations.
• Help define AI evaluation approaches and quality measurement strategies for customer-facing AI experiences.
• Support the creation of test sets, feedback loops, and success metrics tied to accuracy, containment, customer satisfaction, business impact, and adoption.
• Assist in designing contextual AI experiences that use real-time data, conversation state, customer information, interaction history, and external signals to create more relevant and personalized customer and employee experiences.
• Support solution design decisions related to scalability, latency, reliability, and cost efficiency in enterprise environments.
• Help evaluate trade-offs across model selection, caching, orchestration, architecture patterns, and production-readiness considerations.
• Leverage strong working knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support presales activities including technical discovery, solution design, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, success criteria, and differentiated Genesys AI value.
• Partner with account teams and Professional Services to help transition successful prototypes into production pilots, including technical handoff, solution hardening, KPI alignment, and business outcome validation.
• Build and support integrations to third-party systems via RESTful APIs and emerging interoperability patterns, showcasing what is possible with Genesys Cloud AI solutions.
• Develop reusable technical assets and help enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide field feedback and customer insights to Product Management and Engineering related to AI product design, implementation considerations, and customer-driven enhancements.
• Engage with technical and business stakeholders to position Genesys AI as part of a customer’s broader customer experience, IT, and transformation strategy.

AI Architect

• Lead the design, development, and implementation of sophisticated AI/ML, Generative AI, and Agentic AI solutions
• Architect and develop end-to-end AI systems
• Develop, fine-tune, and optimize Large Language Models (LLMs)
• Drive the implementation and experimentation with advanced generative AI methods
• Own the deployment pipeline for AI models and associated services
• Apply deep knowledge of database technologies to design efficient data storage, retrieval, and management strategies
• Contribute to the establishment and enforcement of engineering best practices
• Stay abreast of the latest advancements in AI/ML, cloud technologies, and DevOps trends
• Ensure strict adherence to ethical AI guidelines and compliance standards throughout the entire AI solution lifecycle
• Act as a mentor for junior engineers

Senior AI/ML Engineer – Engineering Excellence, Full Stack Developer, Vice President

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Machine Learning Engineer, Conversion ML

• Lead the development of advanced AI and machine learning systems
• Design end-to-end AI solutions, optimize model performance
• Mentor junior engineers and support cross-team collaboration
• Document architectural decisions and best practices

Senior AI Engineer

• Develop and keep relevant a vision for team in a fast-paced, complex and evolving arena.
• Foster a high-performing team of AI/ML engineers and drive a culture of excellence.
• Define and execute a comprehensive MLOps roadmap.
• Manage a high-performing team in a matrixed environment.
• Design, build, and champion a library of reusable building blocks for scientists and modelers.
• Champion the adoption of a portfolio-wide metrics process to increase visibility of KPIs.

Director, Decision Science AI/ML Engineering – Ops

• 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

• Designing and implementing AI and machine learning solutions to transform raw data into actionable insights
• Developing scalable AI systems using advanced algorithms and software engineering techniques
• Collaborating with clients to understand their needs and deliver tailored data solutions
• Utilizing data wrangling and integration skills to prepare data for AI model deployment
• Applying machine learning libraries such as TensorFlow and Scikit-Learn to enhance model performance
• Conducting complex data analysis to inform decision-making and drive business growth
• Building and maintaining data pipelines and infrastructure to support AI initiatives
• Leveraging programming languages like Python and Java for AI system development
• Engaging in continuous learning to stay updated with the latest AI and machine learning advancements
• Mentoring junior team members and guiding them in AI project execution

Senior Associate – Risk Architecture, Agentic AI, Machine Learning Developer

• 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

• 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, develop, and implement GenAI solutions for various financial applications, including personalized recommendations, risk assessment, fraud detection, and automated reporting.
• Explore and experiment with advanced GenAI concepts like Agentic AI.
• Design and implement intelligent chatbots.
• Process and analyze large datasets of structured and unstructured financial data.
• Architect and implement efficient RAG pipelines, leveraging tools like LlamaIndex and LangChain.
• Develop and refine advanced prompting strategies for LLMs.
• Test, evaluate, and analyze the performance of LLM and other GenAI models.
• Collaborate closely with engineering teams to deploy and maintain GenAI models in production environments, including containerization, CI/CD pipelines, and cloud infrastructure management.
• Communicate effectively with business stakeholders.
• Stay up-to-date with the latest advancements in GenAI research and development, including areas like Agentic AI.

Gen AI-ML Engineer – AVP

• work to design, deploy, and optimize Generative AI solutions
• build agentic workflows and RAG systems
• apply latest advancements in Foundation Models to create AI-native tools
• build and refine LLM-powered features and autonomous agents
• design and implement RAG pipelines to connect proprietary data with LLMs
• explore and eliminate GTM friction points by rapid prototyping AI solutions
• integrate AI workflows into modern sales stack (e.g., Salesforce, Gong)
• develop and maintain production-grade infrastructure for AI orchestration

AI Engineer – Go To Market

• Stay current with advancements in machine learning, GenAI, and cloud-native tooling
• Contribute to technical strategy, patent filings, and the development of reusable frameworks
• Lead design and rapid prototyping of GenAI-enabled solutions
• Evaluate and integrate LLMs and modern GenAI techniques into enterprise use cases
• Lead architecture discussions, conduct model performance evaluations, and implement best practices in MLOps
• Design and build reusable PoCs, pilots, and platform components leveraging modern GenAI/ML frameworks
• Work closely with business groups to understand their business objectives, use cases and challenges
• Embed directly with teams including product managers, researchers, and engineering teams
• Develop and maintain automation and AI platform components
• Mentor junior engineers and foster a culture of experimentation, documentation, and continuous learning

Staff GenAI/ML Engineer – Emerging Tech, AI Automation

• Design, build, and operate end-to-end machine learning and artificial intelligence pipelines supporting batch, streaming, and real-time inference use cases
• Automate the full machine learning lifecycle including ingestion, feature engineering, training, validation, deployment, monitoring, and retraining
• Implement CI/CD pipelines for machine learning systems with automated testing, validation gates, and controlled model promotion
• Develop orchestration workflows using tools such as Airflow, Kubeflow, and MLflow for experiment tracking and governance
• Optimize artificial intelligence workloads for performance, scalability, and cost efficiency using distributed compute and cloud-native services
• Establish monitoring and observability frameworks including performance metrics, data quality checks, drift detection, and bias monitoring
• Design automated retraining strategies including trigger-based, schedule-based, and performance-based refresh cycles
• Create repeatable prompting frameworks and artificial intelligence guardrails to support safe and effective AI-assisted development
• Implement access controls, secrets management, compliance standards, and security best practices across machine learning and artificial intelligence platforms
• Evaluate and operationalize emerging artificial intelligence technologies and vendor tools, identifying measurable business value
• Mentor engineers and data scientists on Machine Learning Operations best practices and influence enterprise-wide architectural standards

AI Commercial, ML Ops Engineer

• 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

Product Owner – Agentic AI Platform

• 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

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