Machine Learning Engineer

• 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

AI Engineer

• 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

• Lead the architecture and delivery of enterprise-grade LLM and agentic AI systems that transform claims, risk, and operational workflows.
• Define technical strategy for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation.
• Design and implement advanced agentic systems capable of planning, reasoning, tool selection, execution, reflection, and recovery.
• Architect stateful, memory-aware AI systems that manage long-running claims processes across multiple touchpoints.
• Build multi-agent collaboration models that coordinate coverage analysis, document validation, fraud signals, compliance checks, and decision support.
• Establish orchestration frameworks that manage task routing, context persistence, structured outputs, and failure handling.
• Design secure tool integration layers connecting agents to claims systems, policy platforms, data warehouses, document repositories, and external data services.
• Implement deterministic guardrails, schema validation, and output verification pipelines to reduce hallucination and execution risk.
• Lead development of document intelligence systems leveraging LLMs for summarization, entity extraction, discrepancy detection, and structured data reconstruction.
• Define prompt engineering standards and reusable reasoning templates for consistent, domain-aware outputs.
• Oversee embedding strategies, vector indexing architecture, retrieval optimization, and knowledge grounding approaches.
• Design evaluation frameworks to measure reasoning depth, workflow completion accuracy, hallucination rates, latency, and cost efficiency.
• Implement observability layers that track agent decisions, tool usage, retrieval effectiveness, and drift across models and prompts.
• Drive optimization strategies for token efficiency, caching, batching, and inference scaling.
• Ensure compliance with Responsible AI principles, enterprise governance standards, audit requirements, and regulatory constraints.
• Partner with enterprise architecture, cybersecurity, and data governance teams to define secure deployment patterns.
• Mentor engineers on LLM orchestration patterns, workflow decomposition, and safe agent design.
• Translate executive-level business objectives into scalable AI platform capabilities.
• Lead proof-of-concepts through full production deployment with measurable ROI outcomes.
• Continuously evaluate emerging foundation models, orchestration frameworks, and agent tooling for enterprise readiness.

Senior Applied AI Engineer

• Develop and deploy AI/ML solutions to support semiconductor product development
• Design, code, and maintain software programs and algorithms
• Collaborate cross-functionally with subject matter experts across engineering, product, and service teams
• Identify and communicate actionable insights from large data and metadata sources

Senior Applied AI Engineer – Semiconductor Manufacturing

• 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 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

• Design end-to-end AI systems for customer experience use cases.
• Architect reliable, production-ready AI solutions that go beyond prompt design, combining LLMs, deterministic workflows, tools, and orchestration layers.
• Define how AI interacts across the full journey (self-service, agent copilot, journey management, and back-office automation), including fallback strategies, human handoff, and failure handling.
• Optimize retrieval-augmented generation (RAG) and knowledge architectures to extend Genesys solutions where required .
• Design scalable knowledge and retrieval strategies that ground AI responses in enterprise data.
• Develop approaches for content structuring, chunking, embedding, and ranking to ensure accuracy, relevance, and freshness.
• Partner with customers to align AI outputs with trusted knowledge sources while balancing performance, latency, and governance requirements.
• Establish AI evaluation frameworks and quality measurement strategies - Define how success is measured for AI-driven experiences, including accuracy, containment, customer satisfaction, and business impact.
• Create test sets, evaluation methodologies, and feedback loops to continuously improve performance.
• Translate technical quality metrics into business-relevant outcomes to support customer decision-making and adoption.
• Engineer contextual AI experiences that leverage real-time data and conversation state - Design how AI systems incorporate dynamic context such as customer data, interaction history, and external signals.
• Optimize context management and prompt structure to maximize relevance while managing token limits and response quality.
• Ensure AI interactions remain coherent, personalized, and aligned across channels and touchpoints.
• Design for scalability, latency, and cost efficiency in enterprise environments - Evaluate and optimize AI solutions for real-world constraints, including response time (especially for voice), throughput, and cost at scale.
• Make informed tradeoffs across model selection, caching strategies, and architecture patterns to deliver performant and economically viable solutions.
• Ensure designs meet enterprise expectations for reliability and responsiveness.
• Support Partner Development Lab Co-created Configurations : Work with Genesys Strategic Technology partners and GSI partners to build, refine, and optimize their Genesys Cloud development lab environments with a focus on AI.
• Technical Escalation: Serve as a technical escalation point for difficult or complex integration issues that arise during partner Pre-Sales cycles. Provide advanced troubleshooting and solution development to resolve roadblocks effectively.
• Mentor Genesys Partner’s Solution Consultant teams and act as a technical escalation point for complex technical issues.
• Technical Field Readiness Assistance: Work with GSI and Genesys Strategic Technology partners, Genesys Product Management, Genesys Product Marketing, and Genesys Enablement teams to assist with technical field readiness. This may involve building assets, shared demo environments, videos, and assisting with RFx’s.
• Support Strategic Pre-Sales Engagements: Co-sell with partners and Genesys Account Teams (when requested) in high impact opportunities by leading technical discovery, solution design, demonstrations, workshops, and value articulation.
• Establish best practice frameworks: Create and maintain structured knowledge-sharing model across partners and internal teams including demos, success stories, and reusable assets. Leverage expert-level knowledge of Genesys AI capabilities — including predictive AI and Agentic Virtual Agents —to articulate and demonstrate product value to customers and prospects. Design and deploy AI prototypes in sandbox and/or partner and customer development environments to validate use cases, integrations, latency, and success criteria , and to highlight the differentiated value of Genesys AI. Support partners, Genesys account teams, and professional services to transition successful prototypes into production pilots supporting technical handoff, hardening, KPI alignment, and business outcome validation through hands-on configuration and building of the production pilots. Build integrations to third-party systems via RESTful APIs and emerging interoperability patterns such as MCP and A2A showcasing the art of the possible with Genesys Cloud AI solutions. Develop reusable technical assets and enable Solution Consultants, partners, and account teams through workshops, deep-dive demonstrations, coaching, and scalable technical content. Provide technical feedback and strategic insights to Product Management and Engineering on AI product design, implementation considerations, and customer-driven enhancements. Support partners in influencing CIO/CTO-level stakeholders and position Genesys as integral to an organization’s broader IT and transformation strategy.

Senior Partner AI Architect

• Define and execute the enterprise AI engineering strategy aligned to Sedgwick’s claims, risk, and client service transformation goals.
• Lead the architecture, development, and deployment of applied AI and agentic AI solutions across global operations.
• Build and scale a high-performing AI engineering organization, including Applied AI Engineers, Agentic AI Engineers, ML Engineers, and AI Platform teams.
• Establish standards for LLM integration, retrieval-augmented generation (RAG), multi-agent orchestration, workflow automation, and model lifecycle management.
• Oversee the design of autonomous and semi-autonomous AI systems that support claims intake, coverage analysis, fraud detection, compliance review, and operational optimization.
• Drive enterprise architecture decisions for AI platforms, including model hosting, orchestration layers, vector databases, evaluation frameworks, and observability tooling.
• Ensure scalable, secure integration of AI systems with claims platforms, policy systems, document repositories, and enterprise data environments.
• Define and enforce engineering best practices for prompt engineering, tool use, memory design, guardrails, structured outputs, and deterministic validation.
• Establish governance frameworks for Responsible AI, explainability, auditability, and regulatory compliance.
• Partner with cybersecurity, legal, compliance, and data governance teams to mitigate AI-related operational and regulatory risks.
• Develop robust evaluation and benchmarking methodologies to measure reasoning quality, workflow completion rates, hallucination risk, and system reliability.
• Oversee AI production operations including performance monitoring, drift detection, cost management, and service reliability.
• Translate executive-level business priorities into scalable AI platform capabilities and delivery roadmaps.
• Collaborate with Claims Operations, IT, Digital, and Product teams to identify high-impact AI use cases and drive measurable ROI.
• Lead build-versus-buy decisions for AI tooling, foundation models, orchestration frameworks, and enterprise integrations.
• Manage vendor relationships related to AI platforms, cloud providers, and model providers.
• Drive adoption of AI solutions across adjusters, supervisors, and client-facing teams through strong partnership and change management alignment.
• Mentor engineering leaders and establish a strong culture of technical excellence, innovation, and operational discipline.
• Present AI strategy, progress, risks, and outcomes to executive leadership and board-level stakeholders.
• Develop long-term AI capability roadmaps that position Sedgwick as a technology leader in claims and risk management.

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