SailPoint is seeking a VP, Data/AI Engineering to shape and deliver on the future technology vision for the organization, driving growth from $1B to $5B+ in ARR and establishing the company as a leader in identity and cybersecurity.
Requirements
- 10+ years of experience in software engineering, with a focus on AI and ML technologies, with 5+ years managing large-scale global teams.
- Deep expertise in ML, with a strong focus on NLP, semantic search, and other advanced natural language processing techniques.
- Experience in environments rolling out ML/GenAI solutions to the market in the last 1–2 years and building data platforms that manage real-time and streaming data at a large scale.
- Combined with AI & ML expertise, deep experience with MLOps, data platforms (e.g., Snowflake), data pipelines (e.g., Airflow), and messaging platforms (e.g., Kafka).
- Strong background in data architecture, software architecture, and distributed systems, with experience coordinating technical efforts across global teams.
- Proficiency in Python, Java, SQL, and other relevant programming languages and tools.
- Experience in cloud-native delivery, with a deep understanding of containerization technologies such as Kubernetes and Docker.
Responsibilities
- AI & ML Leadership: Shape the strategic vision and roadmap for AI and ML initiatives, ensuring global alignment with business goals and cutting-edge technology trends.
- Technical Oversight: Provide strong technical leadership in AI and ML engineering globally, particularly in areas like NLP, semantic search, summarization, and data-driven product development.
- System Architecture & Integration: Oversee the design and integration of complex AI and ML systems within their global software architecture.
- System Integrity & Security: Ensure the integrity, performance, and security of AI/ML systems globally.
- Innovation & Continuous Improvement: Drive innovation in AI and ML practices on a global scale, continuously seeking opportunities to improve their technology stack, processes, and methodologies.
- MLOps & Data Platform Collaboration: Collaborate closely with MLOps, Platform Engineering, and Enterprise Data Platform teams to develop and optimize the global AI and ML infrastructure.
- Cross-functional Collaboration: Collaborate closely with cross-functional teams, including product management, product engineering, and other business units, to align AI and ML initiatives with broader global company objectives.
Other
- Strong communication and collaboration skills, with the ability to engage effectively with stakeholders at all levels of the organization.
- Proven ability to attract new talent and elevate existing talent.
- Experience leading teams of 100+.
- Adept at making structural changes or re-engineers the team for significant measurable positive impact.
- Sets benchmarks beyond standard practice to create best-in-class solutions and introduces them across an organization.
