Senior Vice President, Engineering

Automation Anywhere•San Jose, CA

1d•$350,000 - $375,000•Hybrid

About The Position

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry’s first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential. We're seeking an SVP of Engineering to lead our global engineering organization at a defining moment in the convergence of Automation and AI. You will own end-to-end engineering strategy and execution, driving the vision for AI-native offerings at scale, from foundational architecture to AI-powered product delivery. Partnering closely with Product, Design, Data, and GTM leaders, you'll embed intelligence across our full platform, enabling enterprises to build, automate, and operate smarter. You're a deeply technical, hands-on executive with a proven track record of shipping AI-native products at scale, elevating engineering craftsmanship, and turning ambitious AI bets into measurable business outcomes. This is a pivotal moment in Automation and AI, and you'll shape how we lead it.

Requirements

Bachelor’s degree in Engineering, Computer Science, or a related technical field (Master’s degree a plus)
15+ years in software engineering; 7+ years leading large-scale, multi-team organizations at VP/SVP level in enterprise SaaS
Demonstrated success shipping AI-native products in production with strong governance, telemetry, and quality gates—not just integrating AI, but building around it
Deep expertise scaling cloud systems (multi-tenant, high-throughput) on AWS/Azure/GCP; strong instincts for bottlenecks and performance engineering
Proven ownership of reliability and security at scale: SRE practices, SLOs/SLIs/Error Budgets, secure SDLC, vulnerability management, incident response, and postmortems
Track record of portfolio execution with short release cycles, outcome-based planning, and products with demonstrated customer adoption
Mastery of modern engineering: Kubernetes, Docker, microservices, Java and/or polyglot stacks, event streaming, and cloud-native data systems
Hands-on fluency with the AI/ML stack: Python, PyTorch/TensorFlow, vector DBs, feature stores, LangChain/Semantic Kernel, MLflow/KServe, and model evaluation and observability
Strong executive presence: able to influence at CEO/Board level and inspire large, distributed teams with clarity and conviction
Strategic Vision & Enterprise Thinking: Ability to translate business strategy into a clear, forward-looking engineering vision—anticipating market shifts in AI/automation and aligning long-term technical roadmaps to measurable business outcomes
Executive Influence & Stakeholder Leadership: Exceptional ability to influence and partner across C-suite, Board, and cross-functional leaders (Product, GTM, Finance), while representing engineering with credibility, clarity, and confidence
Organizational Leadership & Talent Development: Proven capability to build, scale, and inspire high-performing global teams—developing senior leaders, fostering accountability, and creating a culture of innovation, inclusivity, and engineering excellence
Decision-Making & Operational Judgment: Strong, data-driven decision-making in complex, high-stakes environments—balancing speed, risk, cost, and quality, especially across AI investments, architecture choices, and build-vs-buy tradeoffs
Customer-Centric Mindset & Business Acumen: Deep understanding of customer needs and enterprise requirements, with the ability to convert insights into impactful engineering priorities that drive adoption, value realization, and revenue growth

Responsibilities

Setting the engineering vision and multi-year roadmap aligned to company strategy; translating business goals into clear technical outcomes and AI-driven portfolio priorities
Leading and scaling a multidisciplinary org (Apps, Platform, AI/ML) with VP/Director leaders and 100+ engineers across geographies
Establishing architecture guardrails and clear standards for quality, security, privacy, and reliability—with AI governance built in from the ground up
Partnering with Product & Design on outcome-based roadmaps and customer value hypotheses; shaping pricing and packaging strategy for AI-native features with PM & Finance
Owning SDLC and delivery excellence—predictable releases, high change success rate, and reduced cycle time through robust CI/CD, automation, and trunk-based development at scale
Driving reliability and performance (SLOs/SLIs/Error Budgets, 99.9x+ availability), capacity planning, and cloud cost efficiency through rigorous FinOps and unit economics discipline
Partnering with Product and Data Science to define and ship AI-native capabilities: intelligent agents, automation co-pilots, autonomous workflow orchestration, RAG pipelines, and safety-by-design governance
Building and scaling ML/LLM platform foundations—feature stores, vector databases, prompt and model management, evals, red teaming, and monitoring for drift, toxicity, and PII leakage
Establishing model lifecycle and compliance practices (data provenance, auditability, bias testing, human-in-the-loop review) and optimize latency and cost across hosted and self-managed models
Recruiting, developing, and retaining top-tier engineering leaders; build an inclusive, high-accountability culture that celebrates craftsmanship, AI innovation, and sustainable pace
Implementing succession planning, leadership pipelines, and clear career ladders; raising the bar through calibration, mentoring, and principled performance management
Being a visible technical voice—internally (all-hands, engineering demos) and externally (conferences, customer briefings, analyst interactions)
Serving as a trusted executive partner to Product, Sales, Customer Success, and Support; represent engineering at the Board and with strategic customers as needed
Converting customer feedback into engineering priorities; ensure compliance, data residency, and enterprise-grade requirements are built into the roadmap
Owning engineering budgets, vendor strategy, and build-vs-buy decisions; ensure efficient spend across cloud, data/ML infrastructure, and tooling with measurable ROI
Leading risk management (operational, technical, privacy), quarterly business reviews, and executive reporting with clear KPIs
Leading technical diligence for acquisitions; own integration plans across systems, data, security, and people—aligning product and architecture roadmaps post-close