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

AI Engineer

• 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

• Lead the ideation, development, and productionalization of sophisticated AI solutions tailored for healthcare payer use cases.
• Architect, design, and implement complex, state-of-the-art AI models and algorithms, leveraging Python and cloud platforms like GCP Vertex AI.
• Partner strategically with product teams and business stakeholders to define and prioritize opportunities for applying advanced AI techniques to solve critical business challenges and drive process automation.
• Conduct in-depth research and maintain expertise in the latest advancements in AI, championing the adoption of cutting-edge techniques and methodologies within the team.
• Architect and develop robust, scalable, and efficient systems and code bases for deploying AI models into production environments, ensuring high availability and performance.
• Serve as a key technical advisor to our internal Lines-of-Businesses, understanding complex needs, gathering requirements, and translating them into impactful, actionable AI solutions.
• Define and oversee the evaluation and validation frameworks for AI models, ensuring their accuracy, reliability, fairness, and performance exceed business requirements.
• Mentor and guide junior engineers on best practices in AI development, model implementation, and system design.

Machine Learning Engineer

• Ensuring that machine learning models are deployed efficiently and reliably into production environments
• Continuously monitoring the performance of models to detect issues like model drift and ensure they remain accurate and effective
• Automating the machine learning pipeline, including tasks like data preprocessing, model training, and evaluation
• Working closely with data scientists, software engineers, and IT operations to integrate machine learning models into business processes
• Managing version control for models and ensuring compliance with governance policies
• Identifying and implementing ways to improve the performance and scalability of ML systems
• Exploring cloud tools and technologies that assist data science with implementing their usecases.

Associate MLOps Engineer

• 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

• 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

• Design, train, and deploy machine learning models spanning classical ML (classification, regression, clustering, time-series) and generative AI use cases including LLM-based and agentic applications.
• Build and maintain cloud-native solutions on AWS using containerized architectures (Docker, Kubernetes) to support scalable model serving and data pipelines.
• Own and contribute to the full Model Development Lifecycle (MDLC), including dataset versioning, model versioning, model registry management, and model evaluation frameworks.
• Develop and integrate Python-based ML components that work seamlessly with existing product platforms across multiple business units.
• Collaborate with AI Product Managers across the Insights BU and partner business units (Provider, Payer, Pharmacy) to translate business needs into AI solutions.
• Apply neural networks and deep learning techniques using PyTorch for appropriate use cases alongside scikit-learn-based classical approaches.
• Write robust, production-ready code following engineering best practices; participate in code and design reviews.
• Leverage AI coding tools (such as Claude Code or equivalent) as part of your daily development workflow to improve velocity and code quality.
• Mentor junior engineers and contribute to team knowledge-sharing around ML best practices, tooling, and architecture decisions.
• Support integration of frontend components into ML-powered features where applicable.
• Contribute to retrospectives and team process improvements; actively participate in sprint planning and end-of-iteration demos.
• Adhere to all HIPAA, data governance, confidentiality, and regulatory requirements in all aspects of work.
• Maintain compliance with Inovalon’s policies, procedures, and mission statement, fulfilling responsibilities that support operational and financial success.

Senior Machine Learning 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.
• Leverage expert-level knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support pre-sales activities such as technical discovery, solution design, product demonstrations, sandbox/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, and success criteria, and to highlight the differentiated value of Genesys AI.

Senior AI Architect

• Analyze and model organizational data for AI function to draw business insights
• Apply data extraction, transformation and loading techniques for large data sets
• Develop LLM strategies and enhance AI ML model deployment efficiency
• Design and develop custom ML, Gen AI, NLP, LLM Models for AI ML pipelines
• Work closely with MLOps team to create and maintain evaluation solutions for models
• Collaborate with product teams and stakeholders for deployment in production environments
• Ensure adherence to standards, governance, and best practices in ML model development

Gen AI Engineer

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• Develop and utilize simulations and models
• Evaluate tools, technologies, architectures, models, and test results
• Head meetings regarding AI with external parties and internal business-embedded innovation teams
• Collaborate with lead engineers to define subsystem requirements and acceptance criteria
• Design, develop, test, and maintain artificial intelligence (AI) systems and applications
• Implement machine learning models using programming languages such as Python, R, Java, JavaScript, or C++
• Collaborate with data scientists to develop algorithms for training AI models
• Conduct research on new technologies in AI
• Optimize AI models for performance, scalability, and efficiency
• Integrate AI solutions into various software applications or platforms
• Collaborate with teams to ensure successful deployment of AI projects
• Ensure AI systems are secure, reliable, and adhere to ethical guidelines
• Stay up-to-date on industry trends and best practices in AI development
• Provide technical guidance and mentorship to junior engineers or interns
• Document code, models, and processes for future reference

• 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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