Beam Benefits

Compensation

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The Role: Strategic Impact & Operational Maturity in an AI-First Environment The Director of Engineering Operations is a critical, senior leadership role responsible for the stability, security, and scalability of Beam's technology platform. This leader will be tasked with transforming our core operational teams (Infra, Platform, Security) into proactive, planning-driven functions capable of supporting rapid, enterprise-level growth and AI/ML workflows. This role requires a leader who excels at strategic planning, upward communication, delegation, and instilling process confidence across the entire organization. What You Will Do I. Strategic Planning & Roadmap Ownership Own the Strategic Narrative: Translate strategic technical vision and business goals into clear, compelling, 12-18 month evolution roadmaps for Infrastructure, Platform, and Security. This includes defining the operational architecture required to support Beam's AI Core (e.g., dedicated GPU/MLOps infrastructure) and presenting to executive leadership and governing committees. Proactive Investment Planning: Drive the shift from reactive firefighting to proactive, back-planned investment by presenting clear proposals for platform improvements and capital expenditure to the executive team. This includes forecasting resource needs for scaling LLM endpoints and predictive data pipeline costs. Elevate Visibility: Ensure all critical operational functions, stability metrics (SLOs), and security posture data are clearly communicated to all cross-functional stakeholders with confidence and clarity, including the performance metrics and latency of core AI services. II. Operational Excellence & Process Maturity Process Maturation: Define, implement, and govern enterprise-grade operational practices across the entire engineering organization, focusing on deployment hygiene, comprehensive testing, incident management, and auditability. This includes: AI Tooling Integration: Mandating the secure adoption and integration of AI coding tools (e.g., Cursor, Augment Code, Gemini) within the Platform and SRE teams to accelerate delivery and improve code quality. Automated Compliance: Automating security and compliance checks in the pipeline, particularly for AI-generated code and data governance requirements (e.g., PII masking). Stakeholder Confidence: Serve as the primary technical point-of-contact for operational assurance with major partners, instilling confidence in Beam's process maturity and delivery predictability, especially in managing the data synchronization pipelines feeding AI models. IT & Infrastructure Oversight: Oversee the Infrastructure, SRE, and IT functions, ensuring high system reliability and efficient internal support while driving down technical debt. This includes building scalable API services and managing the provisioning and maintenance of MLOps environments and data science tooling. III. Leadership, Delegation, & Team Empowerment Scale Through Delegation: Serve as a coach to your direct reports, mentoring them to own decision-making and project execution. You must model and enforce healthy boundaries, delegating tasks effectively to increase the teams' capacity and empowerment. This includes empowering leads to manage the operational aspects of MLOps pipelines and AI deployment lifecycles. Talent Development: Identify and develop high-potential leaders within the operational teams. This includes upskilling the SRE/Platform teams on MLOps principles and cloud-native AI services to support Beam’s future product roadmap. Objective Talent Assessment: Manage the performance review cycle by delivering objective, growth-focused feedback, ensuring all talent is assessed accurately against measurable standards to maximize their professional development. This includes evaluating engineers on their effective and secure use of AI coding assistants. What You Will Bring 10+ years of progressive experience in software engineering, with at least 6+ years of experience leading engineering teams and managing managers. Proven experience managing core infrastructure and SRE/DevOps teams in a cloud-native environment (AWS, GCP, or Azure). Deep, practical experience in MLOps, managing machine learning infrastructure (e.g., Kubeflow, Vertex AI), or scaling LLM endpoint consumption. Deep expertise in cybersecurity best practices, compliance frameworks (SOC2, HIPAA compliance a strong plus), and modern security engineering, with an understanding of AI security risks (e.g., model drift, prompt injection). Demonstrated ability to create and execute strategic, multi-quarter roadmaps that align technical initiatives with overarching business goals and revenue creation. Exceptional communication skills, particularly the ability to communicate technical trade-offs, risks, and strategic vision with confidence to both technical staff and non-technical executives. A history of successfully delegating complex projects, mentoring emerging leaders, and transforming tactical teams into predictable, planning-driven organizations. Strong understanding of what it takes to scale a modern software development life cycle (SDLC) pipeline, including testing and deployment strategies for AI-infused applications. A commitment to high ethical standards and fostering a resilient, high-GRIT culture.