AI/ML Cybersecurity Engineer - Onsite Jersey City, NJ - Tampa, FL - or Phoenix AZ
Job Description
JD
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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.
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Qualifications
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Experience in Python with hands-on in data ingestion, cleaning, machine learning libraries preferably in Scikit-Learn, Pytorch, system and web-based integrations.
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AI Frameworks: Proficiency with LangChain, CrewAI, or AutoGPT for agent orchestration.
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Machine Learning: Hands-on experience in using and tuning Machine Learning models for wide range of cyber security use cases.
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3-4 years of hands-on experience with at least on the cloud AI solution (AWS Bedrock/SageMaker, Azure AI)
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DevOps: Docker, Kubernetes, and Git for version control and deployment.
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Deep experience with RESTful and GraphQL APIs to connect disparate systems.
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Key Responsibilities
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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.
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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.
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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.
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LLM Integration: Integrate large language models (OpenAI, Anthropic, or open-source models into production environments via APIs or local deployments.
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Data Pipeline Orchestration: Develop and optimize data ingestion pipelines, specifically focusing on Vector Databases
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Process Mapping & Optimization: Audit existing manual workflows and re-engineer them using a ??Machine-First?? mindset, utilizing LLMs to handle unstructured data.
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Infrastructure & MLOps: Deploy and monitor AI solutions using cloud-native tools, ensuring high availability, low latency, and cost-efficiency.
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Safety & Compliance: Implement guardrails for AI outputs to ensure ethical standards, data privacy, and ??Human-in-the-loop?? checkpoints where necessary.