In this high-visibility leadership role, you will head a multi-disciplinary R&D group comprising Data Engineering, MLOps, and Data & AI Architecture teams.
You will own the foundational data platforms, operational machine learning lifecycle (MLOps), and enterprise-grade AI architectural patterns that power next-generation security intelligence, Agentic workflows, and predictive cyber defenses across our company products. As a manager of managers and technical vision holder, you will bridge cutting-edge AI research and robust enterprise-scale software engineering, translating complex business objectives into production-grade AI platforms with measurable impact.
Your Impact
Multi-Disciplinary Leadership: Lead, scale, and mentor a high-performing engineering group consisting of Data Engineers, MLOps Engineers, and Data/AI Architects; foster a culture of technical excellence, continuous learning, and rapid, disciplined execution.
End-to-End AI & Data Lifecycle: Own the end-to-end engineering lifecycle for our AI and data ecosystem-spanning large-scale data ingestion and transformation, feature pipelines, model fine-tuning and hosting, CI/CD for ML, inference serving, and real-time observability.
Architectural Strategy: Partner closely with Data and AI Architects to set the technical blueprint for generative AI, agentic frameworks, retrieval-augmented generation (RAG), and unified data lakehouse infrastructures across cloud environments.
MLOps & Operational Excellence: Direct the implementation of automated, secure, and cost-effective MLOps infrastructure (evaluation, guardrails, deployment, monitoring, drift detection, and feedback loops) to reliably scale models from proof-of-concept to global production.
Strategic Collaboration & Executive Alignment: Act as a critical technical partner to Product Management, Threat Research, Security Operations, and core engineering leadership, aligning group roadmaps with company-wide strategic bets.
AI Governance & Security: Champion security, data privacy, and ethical AI standards across all platforms, ensuring resilience against adversarial attacks and compliance with enterprise data policies.
Requirements: Your Experience
Proven Leadership Track Record: 5+ years of direct engineering management experience, with at least 2-3 years managing other managers or multi-team groups (Group Manager, Director, or equivalent) within complex R&D environments.
Deep AI/ML & Data Engineering Depth: Broad, battle-tested technical expertise across modern AI domains (Generative AI, Agentic architectures, LLMs, NLP, traditional ML) combined with enterprise data engineering paradigms (distributed data processing, streaming, modern data lakehouse architectures).
MLOps & Platform Scale: Demonstrated success designing and operating production-scale MLOps platforms, CI/CD for AI, model registries, automated evaluation benchmarks, and low-latency inference serving.
Cloud Architecture & Infrastructure: Strong architectural fluency with major public cloud ecosystems (AWS, GCP, or Azure), containerization (Kubernetes, Docker), and distributed compute/data technologies (e.g., Spark, Kafka, Snowflake, Databricks, Ray).
Execution & Organizational Maturity: Proven ability to balance long-term architectural investments with short-term business deliverables, managing multiple parallel workstreams with transparent governance and numerical KPIs.
Education: B.Sc. or M.Sc. in Computer Science, Software Engineering, Data Science, or an equivalent quantitative discipline.
What Will Make You Stand Out
Direct domain experience in Cybersecurity, threat intelligence, or AI-powered automated security operations (SecOps).
Deep practical knowledge of modern GenAI frameworks and protocols (e.g., LangChain, LlamaIndex, Model Context Protocol (MCP), Vector databases, and fine-tuning techniques).
Hands-on familiarity with native cloud AI suites (e.g., AWS Bedrock/SageMaker, GCP Vertex AI, Azure OpenAI Service).
This position is open to all candidates.