Data Architect & AI Engineering Lead, Cybersecurity - Assistant Vice President

Overview

Provide technical and strategic leadership for enterprise cybersecurity data, analytics, and AI capabilities, driving the architecture and delivery of scalable data platforms, advanced analytics, machine learning, graph analytics, NLP, and Generative AI solutions.

What you'll do

• Design and deliver scalable data pipelines, Lakehouse architectures, data models, and analytics-ready datasets using Python, SQL, PySpark, Databricks, and cloud technologies.
• Build reusable frameworks for data ingestion, transformation, aggregation, data quality, reconciliation, orchestration, lineage, and observability.
• Develop statistical, machine-learning, and AI models to identify patterns, anomalies, relationships, threats, and risk signals across cybersecurity datasets.
• Apply ML, graph analytics, NLP, and Generative AI to improve threat detection, vulnerability prioritization, incident enrichment, cyber-risk scoring, and security posture insights.
• Develop AI solutions leveraging LLMs, embeddings, semantic/vector search, RAG, and intelligent workflows to summarize, classify, correlate, and enrich cybersecurity information.
• Operationalize data science and AI solutions through feature pipelines, MLOps, CI/CD, automated testing, model monitoring, and production workflows.
• Engineer and analyze high-volume structured, semi-structured, time-series, graph, and unstructured security data across cloud and enterprise platforms.
• Build Power BI dashboards and self-service analytical products to communicate security trends, operational performance, risk insights, and model outcomes.
• Establish strong engineering and responsible AI practices covering performance, scalability, resiliency, explainability, security, privacy, lineage, governance, and auditability.
• Partner with cybersecurity, data and platform engineering, architecture, governance, risk, and product teams to translate complex security challenges into scalable data and AI solutions.
• Evaluate emerging data engineering, ML, GenAI, graph, cloud, SIEM/SOAR, and cybersecurity technologies and drive their practical adoption.

What you'll need

• 15 to 20 years of professional experience across data engineering, data science, analytics, cloud, machine learning, AI, or related technology disciplines.
• Experience applying data engineering, analytics, ML, or AI techniques to cybersecurity, risk, fraud, identity, infrastructure, or other large-scale enterprise datasets.
• Bachelor's degree in computer science, Data Science, Statistics, Mathematics, Engineering, Cybersecurity, Information Systems, or equivalent experience.
• Strong hands-on experience with Python, SQL, PySpark, Databricks, ETL/ELT, distributed processing, and large-scale data engineering.
• Experience with Lakehouse/data platform architecture, data pipelines, modeling, data quality, monitoring, lineage, and performance optimization.
• Working knowledge of AWS or comparable cloud data and analytics environments.
• Experience operationalizing analytical and ML solutions using CI/CD, version control, testing, monitoring, and MLOps practices.
• Experience with Power BI or equivalent visualization technologies.
• Strong written, verbal, analytical, and stakeholder communication skills.
• Strong technical expertise across Data Engineering, Data Science, Machine Learning, and AI/GenAI.
• Deep understanding of Lakehouse architectures, distributed data processing, ETL/ELT, data modeling, data quality, orchestration, and pipeline optimization.
• Experience with statistical modeling, feature engineering, anomaly detection, supervised and unsupervised learning, experimentation, and model evaluation.
• Practical knowledge of LLMs, RAG, embeddings, vector search, NLP, graph analytics, and responsible AI practices.
• Understanding of cybersecurity concepts including security telemetry, threat detection, vulnerability management, identity risk, threat intelligence, incidents, and SIEM/SOAR workflows.
• Ability to lead complex technical initiatives, influence architecture and engineering decisions, and communicate effectively with technical teams and senior stakeholders.

Nice to have

• Master's degree or advanced coursework in Data Science, Computer Science, AI/ML, Statistics, Applied Mathematics, Cybersecurity, or related disciplines.
• Experience with Databricks/Delta Lake and Medallion architecture patterns.
• Experience with LLMs, RAG, embeddings, vector search, prompt engineering, NLP, knowledge graphs, entity resolution, or AI-assisted workflows.
• Experience developing production-grade ML/AI pipelines, model monitoring, feature pipelines, APIs, or MLOps solutions.
• Experience working with cybersecurity datasets such as endpoint telemetry, authentication events, cloud security logs, vulnerability data, asset inventories, network events, incidents, SIEM data, and threat intelligence.
• Experience in financial services, regulated industries, or enterprise environments with strong security, privacy, governance, and audit requirements.
• Relevant Security+, CySA+, GIAC, CISSP, AWS, Databricks, Data Engineering, ML/AI, or cybersecurity certifications.

Details

• Location: Bangalore, India.
• Individual contributor role with strong cross-functional collaboration expectations.
• Role requires sound judgment when working with sensitive cybersecurity, risk, operational, and regulated data.
• Candidate should be comfortable balancing exploratory data science, production-minded analytical delivery, and stakeholder communication.