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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Salesforce Developer - **Company:** CareerCircle - **Location:** Hershey, PA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Audit Trail, Cloud Computing, Information Systems, Computer Programming, Continuous Integration, Information Engineering, Data Governance, DevOps, Distributed Systems, Python (Programming Language), Machine Learning, Meta-Data Management, Role-Based Access Control, Reliability Engineering, Azure Machine Learning, Software Engineering, SQL Databases, Workflow Management Systems, Enterprise Data Management, Delivery Pipeline, Large Language Models, Generative AI, Infrastructure as Code (IaC), AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Rancher, Machine Learning Operations, Databricks - **Published:** August 25, 2026 - **Apply:** https://www.careercircle.com/jobs/all/all/usa/pa/hershey/d943f0fa-79df-4a5d-b0af-e6d2e94da4c3 ## About the Role * 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. Education: * Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, or a related technical field. * Advanced degree preferred but not required. ## Description DevOps, Auditing Analytics AI Agents Pipelines Automation Governance Kubernetes Databricks Scalability AI Adoption Supply Chain Data Science Collaboration Mental Health User Feedback AWS SageMaker Responsible AI Data Governance Services Design Computer Science Machine Learning Data Engineering Security Controls Anomaly Detection Value Propositions Demand Forecasting Rancher (Software) Workflow Management Systems Engineering Resource Management Metadata Management Security Management Time Off Management Software Engineering Software Development Cloud Infrastructure User Experience (UX) Lifecycle Management Distributed Computing Predictive Maintenance Azure Machine Learning Artificial Intelligence Infrastructure Security Self Service Technologies SQL (Programming Language) Product Family Engineering Infrastructure as Code (IaC) Python (Programming Language) Role-Based Access Control (RBAC) Generative Artificial Intelligence MLOps (Machine Learning Operations) Cloud Financial Management (FinOps) Artificial Intelligence Infrastructure Application Programming Interface (API), The Staff Data Engineer is a critical member of Hershey's Enterprise Data & Analytics organization, responsible for delivering a seamless, governed, and scalable AI platform experience across the enterprise. This role bridges AI engineering, platform engineering, and product thinking to ensure AI and data tools are easy to discover, adopt, and operationalize. You will own the lifecycle of AI platforms including Databricks, Azure ML, and complementary technologies by evaluating new capabilities, implementing governance controls, enabling self-service access, and continuously optimizing the user experience. Working closely with data scientists, AI engineers, software developers, security teams, and data governance leaders, you will help accelerate AI adoption across manufacturing, supply chain, quality, and commercial use cases while ensuring compliance, security, and cost efficiency. This position is ideal for candidates with backgrounds in AI engineering, platform engineering, MLOps, DevOps, SRE, or data engineering who are passionate about building platforms that empower others to innovate. What will you do? * 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., Auditing Analytics AI Agents Pipelines Automation Governance Kubernetes Databricks Scalability AI Adoption Supply Chain Data Science Collaboration Mental Health User Feedback AWS SageMaker Responsible AI Data Governance Services Design Computer Science Machine Learning Data Engineering Security Controls Anomaly Detection Value Propositions Demand Forecasting Rancher (Software) Workflow Management Systems Engineering Resource Management Metadata Management Security Management Time Off Management Software Engineering Software Development Cloud Infrastructure User Experience (UX) Lifecycle Management Distributed Computing Predictive Maintenance Azure Machine Learning Artificial Intelligence Infrastructure Security Self Service Technologies SQL (Programming Language) Product Family Engineering Infrastructure as Code (IaC) Python (Programming Language) Role-Based Access Control (RBAC) Generative Artificial Intelligence MLOps (Machine Learning Operations) Cloud Financial Management (FinOps) Artificial Intelligence Infrastructure Application Programming Interface (API) +0 Google IT Automation with Python ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Got AI ideas but no money? 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