Role Description Highly skilled Salesforce Agentforce Specialist to lead the design, implementation, and governance of our autonomous AI agents. In this role, you will transform our customer service, sales, and operations by building intelligent agents that do not just chat, but actively execute tasks and workflows. You will bridge the gap between business requirements and technical AI orchestration—leveraging Data Cloud, building precise prompts, and designing multi-agent environments while adhering to strict trust and security standards. Qualifications - Experience in Salesforce Agentforce - Strong understanding of AI orchestration - Ability to design multi-agent environments - Knowledge of Data Cloud - Experience in customer service and sales transformation Requirements - Proven track record in leading design and implementation projects - Ability to work collaboratively with cross-functional teams - Strong analytical and problem-solving skills - Adherence to trust and security standards Benefits - Inclusive workplace culture - Opportunities for personal and professional growth - Commitment to well-being and work-life balance - Equal employment opportunity (EEO) and affirmative action employer

Role Description We are seeking a Senior Platform Engineer who can own infrastructure architecture, reliability, scalability, and platform operations across our cloud environments. This is not a traditional "pipeline management" role. We are looking for someone who can make architectural decisions, evaluate tradeoffs, and build platforms that support large-scale SaaS applications and AI workloads. You will work closely with Engineering, Product, and AI teams to design systems that are secure, resilient, scalable, and cost-efficient. Key Responsibilities - Platform Architecture & System Design - Design and evolve cloud-native platform architecture supporting multi-tenant SaaS applications - Define infrastructure standards, deployment patterns, and platform best practices - Lead architecture reviews and evaluate technical tradeoffs across reliability, performance, security, and cost - Design highly available and fault-tolerant systems across multiple environments - AWS Infrastructure - Architect and manage large-scale AWS environments - Design networking architectures including VPCs, subnets, security groups, routing, load balancing, and connectivity patterns - Build secure deployment architectures aligned with security and compliance requirements - Implement disaster recovery, backup, and business continuity strategies - Kubernetes & Platform Operations - Design and operate production Kubernetes environments - Build scalable container orchestration strategies - Optimize cluster performance, networking, autoscaling, and workload scheduling - Improve developer experience through platform automation and self-service tooling - AI & GPU Infrastructure - Support AI and ML workloads running on AWS - Design infrastructure for model training and inference workloads - Manage GPU provisioning, utilization, scaling, and cost optimization - Collaborate with AI teams to improve deployment and operational efficiency - Reliability & Performance - Define and measure SLIs, SLOs, and operational metrics - Implement monitoring, observability, logging, alerting, and incident management practices - Drive performance optimization and capacity planning initiatives - Lead root cause analysis and reliability improvement efforts - Infrastructure Automation - Build Infrastructure-as-Code solutions using Terraform - Design and optimize CI/CD pipelines - Automate provisioning, deployments, scaling, and operational workflows - Cost Optimization - Continuously evaluate cloud spending - Develop capacity planning models - Balance performance, reliability, and infrastructure costs Qualifications - 6-8 years of experience in DevOps, Platform Engineering, SRE, or Cloud Infrastructure roles. - Proven experience designing and operating production-scale SaaS platforms. - Strong expertise in AWS architecture, networking, security, and deployment strategies. - Deep hands-on experience with Kubernetes, container orchestration, cluster operations, autoscaling, and workload management. - Experience designing highly available, fault-tolerant, and scalable distributed systems. - Strong understanding of system design, architecture trade-offs, and platform scalability. - Hands-on experience with Infrastructure as Code (Terraform preferred). - Experience building and maintaining CI/CD pipelines and deployment automation frameworks. - Strong Linux, networking, and systems engineering fundamentals. - Experience implementing observability, monitoring, logging, and incident management practices. - Experience with disaster recovery planning, backup strategies, and business continuity design. - Experience with cloud cost optimization, capacity planning, and resource utilization management. - Hands-on experience supporting AI/ML workloads in production environments. - Experience designing, provisioning, and operating GPU-based infrastructure for model training and/or inference workloads. - Experience managing and optimizing AWS Bedrock, OpenSearch, and DocumentDB or equivalent platforms. - Strong scripting and automation skills using Python, Bash, or similar languages.

Role Description We're hiring a Senior Platform Engineer to own the reliability, automation, and scalability of the systems that power Cadence's revenue, growth, implementation, and patient outreach operations. This is a senior individual contributor role for an engineer who approaches business systems with the same discipline applied to production infrastructure - using version control, testing, observability, and continuous improvement to build workflows that hold at scale. You'll work across Salesforce, Iterable, Twilio, internal tooling, and data-driven integrations, partnering with Growth, Marketing, Implementation, Operations, Data, Product, IT, and Security to help Cadence reach and serve more patients. This role will report to our Head of IT. What You'll Do - Own the reliability, scalability, and operational health of Cadence's GTM and patient outreach platforms, including Salesforce, Iterable, Twilio, and related systems. - Design, build, and operate cross-system automations, integrations, and workflows using APIs, webhooks, SQL, Git-based version control, deployment management, and observability tooling. - Improve system reliability by implementing monitoring, alerting, root-cause analysis, and preventative controls that reduce workflow failures and operational risk across patient acquisition and engagement pipelines. - Translate business requirements from Growth, Marketing, Operations, Product, Data, IT, and Security into scalable technical solutions, guiding architecture decisions from discovery through long-term support. - Own complex technical and operational initiatives end-to-end - from problem definition and implementation through launch, ongoing support, and continuous improvement. - Apply AI-assisted development tools to improve delivery velocity, system documentation, and operational efficiency across the platform. Qualifications - 10+ years of experience in platform engineering, systems engineering, automation engineering, DevOps, SRE, business systems engineering, or a comparable technical discipline. - Demonstrated experience applying software engineering or platform engineering principles - version control, testing, deployment practices, environment management - to operational systems and business-critical workflows. - Deep experience building and supporting cross-system automations and integrations using APIs, webhooks, event-driven architectures, automation platforms, and SQL. - Strong debugging and root-cause analysis skills, with the ability to trace issues across systems, integrations, data flows, permissions, and configurations. - Experience with Salesforce DX and Git-based Salesforce development and administration practices, or comparable experience applying engineering discipline to complex enterprise SaaS platforms. - Ability to independently own ambiguous technical problems from architecture through launch, monitoring, and long-term operational support. - Fluency with LLM APIs, prompt engineering, and AI-assisted development tools; demonstrated experience building or evaluating AI-powered systems in production. Requirements - Preferred: Experience with Infrastructure-as-Code tooling (such as Terraform or Pulumi), GTM platforms (such as Iterable, Twilio, HubSpot, or Segment), or prior work in healthcare, patient engagement, or regulated environments is a plus. Compensation The base salary for this role typically ranges between $175,000-$205,000 depending on experience, skills, seniority, and business needs. In addition to base salary, this role may be eligible for equity as part of the total compensation package. Actual compensation may vary by location. Benefits - Competitive pay & equity* - Fully remote - Comprehensive health coverage: Medical, dental & vision - Paid time off - 401k plan + matching - Paid parental leave - Home office stipend *Benefit offerings may vary depending on job profile, job level and worker type.

• Design and evolve cloud-native platform architecture supporting multi-tenant SaaS applications • Define infrastructure standards, deployment patterns, and platform best practices • Lead architecture reviews and evaluate technical tradeoffs across reliability, performance, security, and cost • Design highly available and fault-tolerant systems across multiple environments • Architect and manage large-scale AWS environments • Design networking architectures including VPCs, subnets, security groups, routing, load balancing, and connectivity patterns • Build secure deployment architectures aligned with security and compliance requirements • Implement disaster recovery, backup, and business continuity strategies • Design and operate production Kubernetes environments • Build scalable container orchestration strategies • Optimize cluster performance, networking, autoscaling, and workload scheduling • Improve developer experience through platform automation and self-service tooling • Support AI and ML workloads running on AWS • Design infrastructure for model training and inference workloads • Manage GPU provisioning, utilization, scaling, and cost optimization • Collaborate with AI teams to improve deployment and operational efficiency • Define and measure SLIs, SLOs, and operational metrics • Implement monitoring, observability, logging, alerting, and incident management practices • Drive performance optimization and capacity planning initiatives • Lead root cause analysis and reliability improvement effort • Build Infrastructure-as-Code solutions using Terraform • Design and optimize CI/CD pipelines • Automate provisioning, deployments, scaling, and operational workflows • Continuously evaluate cloud spending • Develop capacity planning models • Balance performance, reliability, and infrastructure costs