Software Engineering Project Manager

Responsibilities

• Define and document high-level and detailed architectures for large-scale distributed systems, platform services, and infrastructure components.
• Design solutions that prioritize scalability, reliability, performance, security, and operational excellence.
• Establish architectural standards, design patterns, and engineering best practices across the organization.
• Evaluate and integrate AI‑assisted design and documentation tools to accelerate architecture review cycles and improve decision quality.
• Provide technical leadership and mentorship to engineering teams throughout the software development lifecycle.
• Drive technical decision‑making, architecture reviews, and technology evaluations.
• Influence long‑term platform and system strategy through thought leadership and hands‑on engagement.
• Champion the adoption of AI‑powered developer tools, including AI coding assistants, automated code review, and intelligent CI/CD systems.
• Partner with product managers, engineering leaders, operations teams, and other stakeholders to understand business requirements and translate them into robust technical solutions.
• Facilitate alignment across multiple teams working on interconnected platforms and services.
• Design and evolve cloud‑native platforms, microservices architectures, and distributed applications capable of operating at large scale.
• Lead efforts related to service reliability, fault tolerance, observability, performance engineering, and operational efficiency.
• Drive adoption of modern platform technologies, automation, and developer productivity tools — including AI‑driven automation, intelligent monitoring, and agentic operations tooling.
• Stay current with emerging technologies, industry trends, and architectural approaches — including large language models (LLMs), AI agents, and AI‑augmented engineering workflows.
• Evaluate and introduce new technologies, frameworks, and patterns where they provide measurable business or technical value.
• Contribute to the organization’s long‑term technology roadmap.
• Identify opportunities to apply AI capabilities — such as Agentforce, Salesforce Einstein, and third‑party AI platforms — to drive platform efficiency and innovation.
• Identify architectural, operational, and scalability risks early and develop mitigation strategies.
• Ensure systems meet availability, resilience, disaster recovery, security, and compliance requirements — including responsible AI governance and AI system risk controls.
• Champion operational excellence and engineering rigor across teams.
• Analyze system performance characteristics and drive optimization initiatives.
• Design systems capable of handling significant growth in scale, traffic, data volume, and complexity.
• Maintain comprehensive architectural documentation and design artifacts.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related field.
• 10+ years of experience designing, building, and operating large-scale software systems and distributed platforms.
• Demonstrated success leading architecture and technical strategy across multiple teams and complex initiatives.
• Experience building highly available, mission-critical systems operating at scale.
• Experience working with or evaluating AI/ML systems, AI-powered developer tooling, or agentic workflows is strongly preferred.
• Deep expertise in distributed systems architecture, cloud-native technologies, and modern software engineering practices.
• Strong experience with public cloud platforms such as AWS, Azure, or Google Cloud.
• Proficiency with containerization and orchestration technologies such as Docker and Kubernetes.
• Strong understanding of networking, security, service discovery, load balancing, DNS, and data management systems.
• Experience with microservices architectures, API design, event-driven systems, and asynchronous communication patterns.
• Familiarity with observability, monitoring, logging, and reliability engineering practices.
• Experience with CI/CD pipelines, infrastructure automation, and Dev Ops methodologies.
• Strong understanding of performance engineering, scalability patterns, and distributed data systems.
• Familiarity with AI and ML concepts — including LLMs, model deployment, inference infrastructure, and AI agent frameworks — as they apply to platform and infrastructure engineering.
• Direct, applied experience with AI-powered tools such as Git Hub Copilot, Agentforce, Einstein, or equivalent AI coding and operations platforms.