AI Engineer

A high-profile, exceptionally well-funded Cybersecurity and AI tech company engineering an autonomous, AI-native platform for Insider Risk detection, automated investigation, and response.

Founded by seasoned serial entrepreneurs and backed by tier-1 global venture capital funds with tens of millions of dollars in capital, the company has built an innovative multi-agent architecture.

The company combines robust financial backing and enterprise credibility with an intimate, agile, and authentic AI-First engineering culture.

Located in the Central District immediately adjacent to primary train transit, operating on a flexible working model.

Role Description-

• Serving as a Senior AI Engineer, joining the foundational technological core to design, evaluate, and scale production-grade generative models and autonomous multi-agent systems.
• Architecting and operationalizing advanced Agentic AI systems, implementing complex agent loops incorporating Tool Use, Dynamic Planning, Working Memory, and Reflection mechanisms.
• Building, optimizing, and evaluating robust Retrieval-Augmented Generation (RAG) pipelines to ensure high precision, context fidelity, and relevant grounding in threat investigations.
• Leading fine-tuning workflows across foundational and open-source Large Language Models (LLMs), adapting models to domain-specific cybersecurity and anomaly detection datasets.
• Establishing rigorous Model Evaluation & Measurement frameworks: identifying subtle failure modes, hallucination patterns, and edge cases to continuously optimize inference accuracy and latency in production.
• Operating in an authentic AI-First development environment, heavily utilizing cutting-edge AI-assisted developer tools (e.g., Cursor, Claude, Codex) across daily engineering and system prototyping.
• Transitioning innovative exploratory research and complex POCs into high-throughput, low-latency production inference services.

Requirements-

• 5+ years of dedicated professional hands-on experience as an ML Engineer, Data Scientist, AI Engineer, or Applied Researcher – Mandatory
• Deep, practical hands-on experience working with LLMs and driving model performance optimization in production environments – Mandatory
• Proven background in Fine-Tuning foundational models for specific downstream applications – Mandatory
• Solid technical mastery of advanced RAG architectures with a proven ability to improve retrieval recall, ranking, and response quality – Mandatory
• Comprehensive experience in LLM Evaluation, Benchmarking, and Error Analysis (systematic failure mode diagnosis and metric validation) – Mandatory
• Strong practical understanding and implementation experience with Agentic AI paradigms (Autonomous Agents, Planning, Multi-Tool Invocation, Memory management) – Mandatory
• Daily proficiency and active utilization of modern AI coding workflows (e.g., Cursor, Claude, Codex) – Mandatory
• Proven track record taking machine learning capabilities all the way from POC to resilient Production scale – Mandatory
• Strong engineering discipline, high autonomy in an AI-first setting, and fluent technical communication in English (written and verbal) – Mandatory