JOB DESCRIPTION
GENERAL SUMMARY
Design, build, and operationalize enterprise-scale data and AI solutions that enable secure, reliable, and business-aligned use of data, machine learning, and generative AI capabilities across the organization. Develop data pipelines, AI-enabled applications, LLM-powered chatbots, retrieval-augmented generation patterns, APIs, and integration services that connect enterprise data sources, ModelOps platforms, and business-facing solutions. Partner closely with data scientists, data engineers, actuarial teams, business stakeholders, platform engineering, information security, and AI enablement teams to translate business requirements into scalable technical implementations. Ensure solutions are developed in alignment with enterprise architecture standards, platform best practices, data governance requirements, security controls, and operational support expectations. Provide hands-on engineering expertise across the solution lifecycle, including design, development, testing, deployment, monitoring, documentation, and continuous improvement.
PRINCIPAL DUTIES AND RESPONSIBILITIES-ESSENTIAL FUNCTIONS
- Data Engineering and Solution Development: Design, develop, and maintain scalable data pipelines, data integrations, and analytical workflows that support AI, machine learning, reporting, and business decision-making use cases. Build reliable ingestion, transformation, validation, and delivery processes across structured and unstructured data sources, including cloud storage, data warehouses, APIs, SharePoint, operational systems, and enterprise knowledge repositories. Apply sound engineering practices for data quality, reproducibility, lineage, observability, performance, and maintainability. Collaborate with data scientists, analysts, and business stakeholders to understand data needs and deliver reusable, well-governed data assets that support production-grade AI and analytics solutions.
- AI Application and LLM Engineering: Build AI-enabled applications, LLM-powered chatbots, intelligent assistants, and automation solutions that interact with enterprise data and business systems. Implement prompt orchestration, context management, retrieval-augmented generation, semantic search, tool integration, function calling, and evaluation patterns to support accurate, secure, and reliable AI experiences. Integrate AI capabilities with application interfaces, APIs, workflow tools, and internal platforms while ensuring appropriate guardrails, monitoring, error handling, and user experience considerations. Continuously evaluate emerging generative AI technologies, frameworks, and patterns to improve solution quality, usability, and scalability.
- ModelOps, MLOps, and Deployment Engineering: Support the implementation of repeatable ModelOps and MLOps practices across the full lifecycle of data science and AI solutions, including development, testing, versioning, deployment, monitoring, and retirement. Build and maintain automated workflows, CI/CD pipelines, model deployment patterns, configuration management, and operational runbooks for AI and machine learning workloads. Collaborate with platform engineering and enablement teams to ensure solutions leverage enterprise ModelOps capabilities consistently and efficiently. Promote reproducible development practices, automated testing, performance measurement, model traceability, and operational readiness for production deployments.
- Application, API, and Platform Integration: Develop application services, APIs, connectors, and integration patterns that enable AI solutions to interact securely with enterprise data, models, applications, workflow systems, and external services. Implement authentication, authorization, logging, auditing, and secure communication patterns in alignment with enterprise standards. Support integration across platforms such as Databricks, Posit, cloud services, data warehouses, source control systems, and AI service providers. Design solutions that are modular, reusable, maintainable, and aligned with enterprise architecture and software engineering best practices.
- Stakeholder Collaboration and Enablement: Partner with business stakeholders, regional data science teams, platform engineering, IT operations, and governance teams to understand requirements, clarify technical options, and deliver solutions that address business needs. Translate business and analytical requirements into technical specifications, engineering tasks, proof-of-concept designs, and production implementation plans. Communicate technical concepts, implementation tradeoffs, risks, and recommendations clearly to both technical and non-technical audiences. Support platform adoption by helping teams understand available capabilities, implementation patterns, and engineering best practices for data and AI solutions.
- Performance, Reliability, and Operational Support: Optimize data pipelines, AI applications, model workflows, and supporting services for performance, scalability, reliability, security, and cost efficiency. Monitor production solutions, troubleshoot issues, analyze root causes, and implement improvements that strengthen operational resilience and service quality. Establish observability practices, alerting, logging, exception handling, and performance measurement for data and AI workloads. Collaborate with operations and platform teams to support incident response, capacity planning, service-level expectations, and continuous improvement of enterprise AI solutions.
- Technical Documentation and Knowledge Sharing: Develop and maintain clear technical documentation, including solution designs, data flow diagrams, API specifications, deployment guides, model integration patterns, operating procedures, and support runbooks. Create reusable examples, templates, standards, and implementation guidance that help teams build consistent and sustainable data and AI solutions. Participate in code reviews, design reviews, technical consultations, workshops, and knowledge-sharing sessions to promote engineering excellence and platform adoption.
- Risk Management, Security, and Compliance: Implement data and AI solutions in alignment with enterprise information security, data privacy, model governance, compliance, and risk management requirements. Apply secure development practices, access controls, data protection patterns, audit logging, monitoring, and traceability throughout the solution lifecycle. Partner with information security, data governance, compliance, and model risk stakeholders to identify, assess, and mitigate technical and operational risks. Ensure solutions support responsible AI principles, appropriate human oversight, explainability where applicable, and reliable operation within highly regulated insurance and financial services environments.
- Maintain regular and predictable attendance.
- Perform other duties as required.
- Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Science, Artificial Intelligence, or related technical field
- 7+ years of experience in data engineering, software engineering, AI engineering, analytics engineering, or related technical roles
- 3+ years of hands-on experience building data pipelines, AI/ML solutions, analytics applications, or cloud-based data services
- Hands-on experience developing production-quality code using Python, SQL, and modern software engineering practices
- Experience working with cloud platforms, enterprise data platforms, APIs, source control, and CI/CD processes
Preferred:
- Master's degree in Computer Science, Information Systems, Software Engineering, Data Science, Artificial Intelligence, or related field
- Experience developing generative AI applications, LLM-powered chatbots, intelligent assistants, or retrieval-augmented generation solutions
- Experience with Databricks, Posit, Azure OpenAI, AWS Bedrock, Azure AI services, MLflow, or similar AI/ML platforms
- Experience in insurance, financial services, or highly regulated industries
- Relevant certifications in cloud, data engineering, AI engineering, Databricks, or software development
Skills and Abilities
Required:
- Strong hands-on engineering skills in Python, SQL, data transformation, API integration, and application development
- Experience building AI-enabled applications using LLM APIs, prompt engineering, context management, retrieval patterns, and evaluation approaches
- Practical knowledge of data engineering patterns, including ingestion, transformation, validation, orchestration, lineage, and data quality controls
- Familiarity with cloud services, distributed computing, containerization, workflow orchestration, and scalable application design patterns
- Knowledge of CI/CD, DevOps, and collaborative development workflows using Git, Jenkins, Databricks Asset Bundles, Azure DevOps, or similar tools
- Understanding of data security principles, including authentication, authorization, encryption, RBAC, secrets management, and secure API design
- Understanding of MLOps/ModelOps practices for versioning, testing, deployment, monitoring, reproducibility, and operational support
- Ability to troubleshoot complex technical issues across data pipelines, AI services, applications, infrastructure, and platform integrations
- Strong analytical, problem-solving, and investigative skills with attention to reliability, maintainability, and operational excellence
- Excellent written and verbal communication skills with ability to explain technical concepts to both technical and non-technical stakeholders
- Ability to translate business requirements into technical designs, engineering tasks, and production-ready implementations
- Ability to quickly learn emerging technologies, manage multiple priorities, and deliver high-quality solutions in a collaborative environment
Preferred:
- Experience with Databricks capabilities such as Unity Catalog, Databricks Asset Bundles, Workflows, MLflow, Apps, Pipelines, Jobs, SQL Warehouse, Delta Lake, and Delta Live Tables
- Experience with Posit Workbench, Posit Connect, R, and integration patterns for data science environments
- Experience with vector databases, embeddings, semantic search, knowledge retrieval, agent frameworks, LangChain, LlamaIndex, or similar AI orchestration tools
- Experience with OpenAI-compatible APIs, AWS Bedrock, model gateways, prompt management, and LLM evaluation frameworks
- Experience with data warehouses, lakehouses, Snowflake, cloud storage, APIs, SharePoint, S3, and enterprise data integration patterns
- Familiarity with monitoring and observability tools such as Prometheus, Grafana, Datadog, CloudWatch, Azure Monitor, or similar platforms
- Understanding of microservices, event-driven systems, serverless architectures, and container platforms
- Knowledge of data governance, metadata management, data catalogs, model governance, responsible AI, and auditability practices
- Experience supporting proof-of-concept assessments, technical evaluations, and implementation of emerging AI technologies
- Knowledge of insurance industry data, actuarial workflows, risk analysis, model validation, and regulatory compliance requirements
PHYSICAL/MENTAL REQUIREMENTS AND WORKING CONDITIONS
The duties listed above are intended only as illustrations of the various types of work that may be performed. The omission of specific statements of duties does not exclude them from the position if the work is similar, related or a logical assignment to the position.
While performing the duties of the job, the employee is frequently required to talk and communicate with others. The employee is regularly required to feel objects, tools, controls or to keyboard. Vision abilities to perform duties such as analyzing data and figures and/or viewing a computer terminal are required.
Associates are not exposed to adverse environmental conditions.
The job description does not constitute an employment agreement between the employer and employee and is subject to change by the employer as the needs of the employer and requirements of the job change.
Approved by:
Position's Supervisor
Next Level of Management
Human Resources Representative
