AI Engineer:

Join our dynamic team at the forefront of cybersecurity innovation, where cutting-edge AI meets real-world challenges. As an AI Engineer, you'll drive the development of sophisticated AI solutions that leverage Large Language Models (LLMs) and intelligent agent systems. Working alongside our AI engineering, R&D, and product teams, you will design, develop, and deploy production-level systems—from initial data collection and model optimization to sophisticated cloud-based deployment and monitoring.

About Cytactic:

Cytactic is defining a new category in cybersecurity - Cyber Incident Response Management (CIRM). As AI-driven attacks accelerate to machine speed, most organizations still lack the tools to manage incidents end-to-end. Cytactic's agentic platform enables real-time, cross-functional response at machine speed - bringing together the people, processes, and data needed to contain and manage cyber incidents effectively, from preparation through resolution.

Key Responsibilities

AI Development:

• LLM Production Solutions: Develop sophisticated LLM-based production-level solutions.
• AI Systems & Agent development: Design, build, and deploy systems, agents, and workflows that enable complex decision-making processes—utilizing modern frameworks (e.g., LangChain, LangGraph) to manage and coordinate multiple AI agents.
• RAG Pipeline Enhancement: Build and optimize Retrieval-Augmented Generation (RAG) pipelines to boost accuracy.
• MCP Integration: Implement Model Context Protocol (MCP) to enable AI systems to seamlessly interact with external tools, data sources, and services, creating extensible and interoperable AI architectures.

Backend & Multi-Service Architecture:

• Microservices Development: Design and implement scalable backend services and microservices architectures that support AI workloads and integrate seamlessly with existing systems.
• API Development: Develop robust, production-ready APIs for AI model serving, inter-service communication, and handling AI inference requests with event-driven, asynchronous patterns.
• Service Integration: Orchestrate multi-service architectures, ensuring efficient communication between AI components, databases, caching layers, and external services.

Cloud Infrastructure & Deployment:

• AWS Service Utilization: Leverage AWS services (ECS, Lambda, S3, Bedrock, Step Functions) to deploy scalable and secure AI applications.

Required Skills:

• Proficiency in Python and modern software engineering practices.
• Hands-on experience with LLMs, prompt engineering, RAG implementation, and agent development.
• Strong working knowledge of AWS services (Specifically Bedrock, Step Functions, ECS, Lambda, S3) and cloud architecture.
• Proficiency with Git and collaborative development workflows.

Bonus points for:

• Familiarity with cybersecurity principles and best practices.
• Strong product sense and ability to translate business needs into LLM implementations.
• Experience working with Multi-Cloud Platforms (MCP).

Qualifications:

Experience:

• 4+ years of experience as a Software Engineer or similar role.
• Including 2+ years of experience specifically as an AI Engineer.

Education:

• Bachelor’s or Master’s degree in a quantitative discipline or a closely related field.

We recognize that not every candidate will meet every qualification listed above. If you’re passionate about AI innovation and believe your skills align with our vision, we encourage you to apply.

Our Culture:

At our innovative startup, we cultivate a collaborative and team-centric environment that values creativity, technical excellence, and continuous learning. We empower our team members to explore new ideas, drive impactful projects from day one.