Job Description

Company Description

AIBound is revolutionizing AI security with the industry's first unified control plane for secure AI adoption. We discover, test, and protect each AI model, agent, and identity—catching AI risks before impact so enterprises can innovate safely and at speed. As AI adoption outpaces security across global organizations, AIBound eliminates the dangerous gap between innovation and protection.

Led by our CEO and founder, the former CISO at Palo Alto Networks and Workday, AIBound brings together a world-class team of cybersecurity veterans who have secured some of the world's most advanced enterprises. We're a fast-growing company backed by leading investors, positioned at the critical intersection of AI innovation and enterprise security—one of the most strategic technology frontiers of our generation.

Join us in building the future of AI security, where cutting-edge artificial intelligence meets battle-tested cybersecurity expertise.

Role

AIBound is building AI security products that rely on complex LLM and agentic workflows running in real enterprise environments. We are hiring a Senior AI Platform Engineer to own the end-to-end platform layer that powers these systems—from data ingestion and retrieval to LLM serving, scaling, and observability.

This role blends LLM-focused data engineering with production MLOps, ensuring our AI systems are reliable, explainable, secure, and cost-efficient. You will work closely with AI researchers, security engineers, and product teams, contributing hands-on while shaping platform standards.

Responsibilities

LLM & Agentic Data Platform

Design and scale RAG pipelines: ingestion, cleaning, chunking, embeddings, indexing, refresh policies
Architect and manage vector data systems (Pinecone / Weaviate / Milvus / FAISS), including metadata modeling and retrieval performance tuning
Build batch and streaming pipelines to support agent memory, tool outputs, and enterprise data sources
Implement data quality, lineage, and observability across AI-facing datasets
Manage prompt datasets, evaluation corpora, and feedback loops (human + automated)
Support agentic workflows, including tool input/output logging, Short-term and long-term memory stores, Orchestration traces and reproducibility

MLOps, Deployment & Reliability

Deploy and operate LLM inference services on GCP using GKE
Own high-performance LLM serving using vLLM (or equivalent), including batching and concurrency tuning
Build containerized services using Docker and FastAPI
Design and operate autoscaling strategies (HPA, GPU-aware scaling, traffic-based scaling)
Implement CI/CD pipelines for models and services with canary and rollback strategies
Set up monitoring, logging, and alerting for latency, errors, throughput, GPU utilization, and cost
Drive security and cost optimization: IAM, secrets management, network policies, caching, right-sizing

Platform Ownership & Collaboration

Define platform standards for LLM data + serving readiness
Partner with AI engineers to ensure models are deployable, observable, and safe by design
Work cross-functionally with Product and Security teams to translate requirements into scalable platform solutions

Qualifications

3–5 years of hands-on experience across data engineering, MLOps, or platform engineering
Strong Python and SQL skills
Proven experience building production data pipelines (batch + streaming)
Practical experience with RAG systems, embeddings, and vector databases
Strong hands-on experience with GCP, especially GKE
Solid understanding of Kubernetes, Docker, and container orchestration
Experience serving LLMs in production using vLLM (preferred) or similar frameworks
Experience with CI/CD, monitoring, autoscaling, and incident response in production systems
Strong understanding of agentic AI concepts (tools, memory, orchestration, failure modes)

Benefits & Culture

Highly competitive salary and equity package
Hybrid work environment (2 days onsite per week), and vacation policy
Comprehensive health benefits
Professional development budget, conference attendance and access to AI research resources.
AIBound is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Qualifications

3–5 years of hands-on experience across data engineering, MLOps, or platform engineering
Strong Python and SQL skills
Proven experience building production data pipelines (batch + streaming)
Practical experience with RAG systems, embeddings, and v

LLM & Agentic Data Platform

Design and scale RAG pipelines: ingestion, cleaning, chunking, embeddings, indexing, refresh policies
Architect and manage vector data systems (Pinecone / Weaviate / Milvus / FAISS), including metadata modeling and retriev

Company Description

AIBound is the control plane for secure AI, helping enterprise security teams detect and prevent AI risks in real time across browsers, endpoints, networks, and cloud.

AIBound - Senior AI Platform Engineer LLM, Agentic Systems & Production MLOps ( 0-0 yrs )