Job Title

Company We partner with enterprises to advise, build, secure, and operationalize AI systems at scale . Our focus is on developing Generative AI (GenAI), Agentic AI, and Reinforcement Learning-driven systems , while embedding security, governance, and risk controls directly into AI workflows . We enable organizations to safely deploy LLMs, autonomous agents, and adaptive decisioning systems in regulated, mission-critical environments. Job Description As a Senior AI Security Engineer (GenAI, Agentic AI & Reinforcement Learning) , you will lead the design and implementation of secure, scalable, and adaptive AI systems , including LLM-based applications, agentic workflows, and RL-driven decision engines . This role goes beyond traditional security—you will build intelligent, self-improving security review systems using agentic frameworks (LangGraph, LangChain, LangSmith) and reinforcement learning techniques to continuously enhance AI risk evaluation, policy enforcement, and approval workflows. You will collaborate closely with AI/ML engineers, platform teams, and governance stakeholders to embed autonomous, learning-based security mechanisms into enterprise AI ecosystems. Key Responsibilities GenAI, Agentic AI & RL Security Architecture Design and secure LLM, RAG, multi-agent, and RL-driven systems Implement security controls for: Autonomous decision-making agents RL-based adaptive systems Tool-using and API-integrated agents Ensure safe exploration and bounded behavior in RL environments Agentic AI + Reinforcement Learning for Security Automation (Core Focus) Build agentic AI pipelines using: LangGraph → multi-step, stateful security workflows LangChain → LLM orchestration and tool integration LangSmith → observability, tracing, and evaluation Develop RL-enhanced security agents that: Learn from past approval decisions Optimize risk scoring and classification over time Continuously improve policy enforcement accuracy Implement feedback loops (human-in-the-loop + automated) to train: Risk evaluation agents Compliance validation agents Automate end-to-end intake → evaluation → approval pipelines for GenAI and Agentic AI use cases Reinforcement Learning Implementation & Governance Design and implement RL models for adaptive security decisioning Policy optimization Risk-based prioritization Dynamic access control adjustments Apply safe RL techniques : Reward shaping aligned with compliance and security policies Constraint-based RL (safe exploration boundaries) Monitor and mitigate risks such as: Reward hacking Unsafe policy learning Drift in learned behaviors Integrate RL models into AI governance workflows for continuous improvement AI Risk, Governance & Compliance Translate frameworks such as: NIST AI RMF EU AI Act OWASP Top 10 for LLMs into automated, adaptive controls Build dynamic risk scoring systems enhanced by RL: Adversarial Risk Score Model Drift Index Policy Compliance Confidence Score Generate real-time AI risk heat maps and approval recommendations Implement policy-as-code + policy-learning systems Security Assessment & Red Teaming Conduct AI/LLM/RL system security assessments Perform red teaming across : Prompt injection scenarios Agent tool misuse RL policy exploitation Evaluate vulnerabilities in: RAG pipelines Multi-agent coordination RL training environments AI/ML Lifecycle & LLMOps/RLOps Security Secure the full lifecycle: Data ingestion, labeling, and validation Model training (LLM + RL) with GPU isolation and sandboxing Deployment, inference, and continuous learning loops Implement RLOps + LLMOps security controls Ensure: Model lineage and provenance Secure feedback loops Version control for policies and learned behaviors Monitoring, Incident Response & Observability Build AI + RL-aware monitoring systems Detect anomalies in: LLM outputs Agent decisions RL policy shifts Develop incident response playbooks for autonomous systems Create executive dashboards linking AI + RL risk to business KPIs Data Security & Access Control Implement fine-grained and adaptive access controls Secure: RAG knowledge bases Vector databases RL training datasets Ensure compliance with data privacy and residency requirements Thought Leadership Act as an SME in: AI Security Agentic AI systems Reinforcement Learning security Research emerging risks in: Autonomous AI systems Self-improving models Multi-agent + RL ecosystems Qualifications Required Bachelor’s degree in Computer Science, Engineering, or related field 3–5+ years of experience in cybersecurity (application, cloud, or data security) Strong experience in automation, scripting, and security tool development Hands-on experience with: GenAI / LLM applications AI threat modeling and risk assessment Deep understanding of AI threat vectors: Prompt injection Data leakage Adversarial attacks Experience with Azure or AWS cloud security ecosystems Preferred (Strong Differentiators) GenAI & Agentic AI Hands-on experience with: LangChain LangGraph LangSmith Experience building agentic workflows and multi-agent systems Experience securing RAG pipelines and LLM applications Reinforcement Learning (Highly Valued) Experience implementing Reinforcement Learning models : Policy optimization Reward function design Decision-making systems Familiarity with: RLHF (Reinforcement Learning from Human Feedback) Safe RL and constrained optimization Experience integrating RL into: Automation workflows Security decision systems Understanding of RLOps pipelines and lifecycle management Security & Governance Familiarity with: OWASP Top 10 for LLMs NIST AI RMF, EU AI Act, ISO 42001 Experience with: Microsoft Sentinel, Azure Monitor, Purview, Key Vault Policy-as-code and automated compliance frameworks Knowledge of data privacy regulations (GDPR, DORA, etc.)