AI/ML Cybersecurity Engineer - Onsite Jersey City, NJ - Tampa, FL - or Phoenix AZ

JD

Seeking a highly motivated Security Engineer to design, develop, and deploy autonomous agents that eliminate manual overhead and drive intelligent decision-making across the organization while ensuring proper guardrails and security controls are implemented.

Qualifications

Experience in Python with hands-on in data ingestion, cleaning, machine learning libraries preferably in Scikit-Learn, Pytorch, system and web-based integrations.

AI Frameworks: Proficiency with LangChain, CrewAI, or AutoGPT for agent orchestration.

Machine Learning: Hands-on experience in using and tuning Machine Learning models for wide range of cyber security use cases.

3-4 years of hands-on experience with at least on the cloud AI solution (AWS Bedrock/SageMaker, Azure AI)

DevOps: Docker, Kubernetes, and Git for version control and deployment.

Deep experience with RESTful and GraphQL APIs to connect disparate systems.

Key Responsibilities

Agentic Workflow Design: Build and maintain AI agents capable of multi-step reasoning and tool-use to automate cyber security use cases such as Autonomous Incident Investigation, vulnerability prioritization.

Advanced Prompt Engineering with expertise in designing high-precision, version-controlled prompts with ability to programmatically optimize LLM outputs for reliable security logic and automated remediation scripts.

Use and fine-tune machine learning models (like Deep Learning, K-MEANS, SVM etc) to identify sophisticated attack patterns that bypass traditional signature-based defenses.

LLM Integration: Integrate large language models (OpenAI, Anthropic, or open-source models into production environments via APIs or local deployments.

Data Pipeline Orchestration: Develop and optimize data ingestion pipelines, specifically focusing on Vector Databases

Process Mapping & Optimization: Audit existing manual workflows and re-engineer them using a ??Machine-First?? mindset, utilizing LLMs to handle unstructured data.

Infrastructure & MLOps: Deploy and monitor AI solutions using cloud-native tools, ensuring high availability, low latency, and cost-efficiency.

Safety & Compliance: Implement guardrails for AI outputs to ensure ethical standards, data privacy, and ??Human-in-the-loop?? checkpoints where necessary.