Sr. Cloud Infrastructure Engineer (AI & LLM Platforms)

We are seeking a specialized Infrastructure Engineer to bridge the gap between our large data repositories, Cloud Platform and the rapidly evolving world of Large Language Models (LLMs). You will be responsible for building the “plumbing” that allows our internal teams and external users to leverage AI effectively. This includes deploying Model Context Protocol (MCP) servers, building agentic execution environments, and scaling our internal Retrieval-Augmented Generation (RAG) architecture., * AI Architecture Guidance: Guide the architecture that will allow us to leverage AI tools with our large existing data stores and incoming streams of realtime intelligence.

• Cross-Team Integration: Work closely with other infrastructure engineers and software development teams to integrate AI tools into existing systems.
• MCP Ecosystem Management: Design, deploy, and maintain Model Context Protocol (MCP) servers to allow LLMs to securely interact with our internal databases, APIs, and external tooling.
• Agentic Infrastructure: Build and orchestrate sandboxed, scalable environments (e.g., using Docker or specialized runtimes) where users can safely build and execute AI agents.
• Internal RAG Platform: Develop and manage the infrastructure for our internal RAG (Retrieval-Augmented Generation) pipeline, including vector database management (e.g., Pinecone, Weaviate, or pgvector) and automated embedding pipelines.
• Deployment & Scaling: Utilize Kubernetes (K8s) and Infrastructure as Code (Terraform/Pulumi) to deploy LLM-related tools, ensuring high availability and low latency for model inference and data retrieval.
• Security & Governance: Implement strict guardrails for data privacy within LLM workflows, ensuring internal datasets remain secure while being accessible to authorized AI tools.

Do you have experience in TypeScript?, Do you have a Bachelor’s degree?, * 5+ years of experience in DevOps, Platform Engineering, or SRE, with at least 1-2 years specifically focused on AI/ML infrastructure.

• Proven track record of building production-grade RAG pipelines or LLM-integrated applications.
• Thrives in “day zero” environments where the tools and protocols (like MCP) are evolving weekly.
• Deep understanding of the security implications of LLMs (prompt injection, data leakage, and secure tool execution).
• Experience working with substantial datasets (over 1bn objects, dozens or hundreds of TBs) and the challenges of leveraging AI tools with these data sets.
• Bachelor’s degree or equivalent in computer science or related field.

Required Technical Skills:

• Cloud & Orchestration: AWS/GCP/Azure, Kubernetes, Terraform, Helm.
• AI Frameworks: LangChain, LlamaIndex, LangGraph.
• Data & Vectors: Pinecone, Milvus, Qdrant, or pgvector; Apache Kafka/Pulsar; Elasticsearch/OpenSearch; traditional SQL RDBMS.
• Languages: Python (Expert), TypeScript/Node.js (for MCP development), Go.
• AI Protocols: Model Context Protocol (MCP), REST/gRPC.

Compensation: We offer a competitive compensation package and comprehensive benefits package. Salary will be determined based on experience, skills and geographic location.

Q6 Cyber was founded in 2016 to address a growing gap in how financial institutions detect and prevent fraud and cybercrime. As the dark web matured into a sophisticated marketplace for stolen data and financial crime activity, the founding team saw that financial institutions lacked visibility into where attacks were being planned and enabled. Q6 was built on the belief that monitoring threat actors and underground ecosystems directly would allow institutions to act earlier, reducing losses before fraud ever reaches customers or systems. The company’s mission has remained focused on proactive intelligence that leads to real prevention, not just detection.

Q6 Cyber delivers a threat intelligence platform focused on monitoring the digital underground, including the dark web and other covert online channels where cybercriminals operate. The platform identifies early indicators of fraud, compromised data, and emerging attack methods, allowing customers to intervene before incidents occur. Unlike traditional security tools that surface alerts after an attack is underway, Q6 emphasizes actionable intelligence that fraud and security teams can operationalize quickly. Its solutions are designed primarily for banks, credit unions, and financial services organizations that need to protect assets, customers, and brand trust.

Growth Stage & Market Differentiation

Both profitable and private equity-backed, Q6 has established strong credibility in a market that highly values proactive fraud prevention. They have helped financial institutions identify and mitigate hundreds of millions of dollars in fraud losses, demonstrating clear and substantial ROI.

It serves a broad range of customers, from smaller financial institutions to large global banks, and operates in a market that remains underpenetrated, as many institutions have not fully adopted dark-web intelligence as part of their security stack.

In August 2025, Q6 Cyber received a strategic growth investment from Charlesbank Capital Partners. The funding is intended to support expansion of the product portfolio, go-to-market scale, geographic growth, and potential acquisitions. This is an exciting time to join the company due to the growth trajectory and aspirations, and associated equity upside.

Rooted in Entrepreneurialism

Q6 consists of a lean, mission-driven team working in a fast-paced, highly collaborative environment. Q6 embodies the following practices:

• Accountability in one’s work
• Meritocracy and a flat organization
• Willingness to take on any challenge and dig into any project
• Building solutions with a real, tangible impact

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