Data & AI Engineer

A highly successful and profitable B2B SaaS startup in the Cybersecurity domain that empowers security and risk management leaders with a clear, defensible view of their cyber exposure.

By mapping technical vulnerabilities to physical attack paths and financial risk, the platform translates complex security telemetry into actionable business intelligence.

Backed by substantial tier-1 funding and a robust portfolio of global enterprise clients, the company combines long-term corporate stability with high-velocity innovation at the absolute intersection of Cyber and Generative AI.

The company's corporate R&D hub is located in the Central District (exceptionally accessible, situated within short walking distance of a primary train station) and operates on a hybrid model allowing for two days of remote work per week.

Position Overview-

• Inaugural Data & AI Tech Lead stepping into a foundational leadership role to architect, build, and scale the core engineering infrastructure that bridges complex cyber datasets with advanced AI systems.
• Designing and implementing high-throughput production Data Lakes and pipelines using Databricks and Spark to process massive streams of batch and real-time security data.
• Architecting next-generation cognitive systems leveraging LLMs, vector databases, and RAG architectures to transform raw security data into automated, intelligent risk insights.
• Engineering sophisticated autonomous workloads using Agentic system design (LangGraph, OpenAI Agents SDK) to pioneer the future of automated threat simulation and vulnerability analysis.
• Championing engineering excellence by driving large-scale AI and data automation pipelines into live production using Python and modern CI/CD patterns.

Requirements-

• 3+ years of proven professional Python development experience within complex, production-grade environments (Mandatory).
• Deep, production-tested expertise in Data Lake architectures, Spark, Kafka, and Databricks handling large-scale data processing (Mandatory).
• Hands-on engineering experience building and deploying LLM-based systems using foundational models (OpenAI, Anthropic, or AWS Bedrock) (Mandatory).
• Comprehensive technical mastery of RAG, Embeddings, Vector Databases, and advanced Prompt Engineering (Mandatory).
• Direct, practical experience with Agentic system design and orchestration frameworks such as LangGraph or equivalent SDKs (Mandatory).
• Active professional familiarity with modern AI-assisted coding environments (such as Claude Code, Cursor, or Codex).
• Absolute mastery of Git, production CI/CD pipelines, SQL, and high-volume real-time data streaming.