Staff Data Engineer, AI Engineering
About the role
The Staff Data Engineer plays a crucial role in Hershey's Enterprise Data & Analytics organization. They are responsible for delivering a seamless, governed, and scalable AI platform experience across the enterprise. This role involves bridging AI engineering, platform engineering, and product thinking to ensure AI and data tools are easy to discover, adopt, and operationalize.
Responsibilities
Own and evolve the AI platform roadmap across Databricks, Azure ML, and related technologies, prioritizing enhancements based on business needs, adoption metrics, and user feedback.
Pilot, evaluate, and operationalize new AI platform capabilities, making recommendations on enterprise adoption and standardization.
Build and maintain governed, scalable AI infrastructure including compute environments, deployment pipelines, monitoring solutions, feature stores, and AI services.
Design and implement "paved road" solutions that provide secure, compliant, and repeatable workflows for AI and machine learning development.
Embed AI Trust, Risk, and Security Management (AI TRiSM) practices into platform processes, including monitoring, auditability, access controls, and model governance.
Develop self-service experiences that enable teams to discover approved models, datasets, templates, tools, and AI services.
Optimize platform performance and cloud spending through FinOps practices, automation, usage analytics, and resource management.
Partner with Data Governance, Security, Infrastructure, and Analytics teams to ensure platform capabilities support enterprise standards.
Support AI initiatives focused on manufacturing and supply chain challenges including predictive maintenance, demand forecasting, anomaly detection, and quality optimization.
Foster AI adoption through communities of practice, champion networks, training, and continuous feedback loops.
Requirements
Minimum Education & Requirements: 3-5+ years of relevant experience in platform engineering, AI engineering, ML engineering, data engineering, DevOps, SRE, or related technical disciplines, aligned to Hershey Level 50 requirements. Hands-on experience with Databricks, Azure ML, AWS SageMaker, or comparable enterprise AI/ML platforms. Experience implementing MLOps practices including CI/CD, model deployment, monitoring, automation, and lifecycle management. Knowledge of cloud infrastructure, Kubernetes, distributed computing, infrastructure-as-code, and platform automation. Experience with data governance, security controls, RBAC, audit logging, metadata management, and compliance processes. Strong programming and automation skills using Python, SQL, APIs, and modern software engineering practices. Experience managing cloud costs and implementing FinOps best practices for AI workloads. Ability to collaborate effectively across technical and business teams while translating complex concepts into practical solutions.
Preferred Qualifications: Experience supporting manufacturing, supply chain, retail, or CPG environments. Experience building self-service developer platforms or internal engineering platforms. Exposure to generative AI, AI agents, LLM orchestration frameworks, and emerging AI technologies. Product-oriented mindset with experience managing roadmaps, adoption metrics, and user feedback programs. Familiarity with responsible AI frameworks, AI governance, and AI TRiSM principles.
Qualifications
Education: Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, or a related technical field. Advanced degree preferred but not required.
Benefits and Perks
At Hershey, we offer competitive pay and a comprehensive total rewards package designed to support your wellbeing, financial security, and life outside of work. Health & Wellbeing: Medical, dental, and vision coverage, plus wellness programs that support your physical and mental health. Financial Security: Competitive pay, annual incentive opportunities, and a 401(k) with company match. Time Off & Flexibility: Paid time off, company holidays, and flexible ways of working where applicable. Growth & Development: Career development programs, learning opportunities, and internal mobility.