AI Platform Engineer Full Time

Company Description

Sentinai is a defense-grade intelligence platform built for real-time threat detection and rapid response. Leveraging advanced AI, Sentinai delivers autonomous visual intelligence to secure government facilities, infrastructure, and mission-critical environments. Our systems integrate seamlessly with existing security infrastructure, providing robust, edge-deployed, and cloud-independent operations. Designed with military-grade rigor, Sentinai combines ethical standards with precision detection to safeguard environments where safety is non-negotiable.

Role Description

This is a part-time, remote role for a Senior Backend Engineer – Real-Time Systems & Distributed Inference. The engineer will be responsible for architecting Sentinai’s real-time data and video intelligence backend, enabling multi-camera ingestion, rapid inference orchestration, and low-latency event reasoning across edge-deployed environments.

Responsibilities include building distributed pipelines for video metadata, integrating GPU inference services (Triton/TensorRT/DeepStream), implementing scalable event processing systems, and designing APIs powering multimodal AI reasoning. This position requires close collaboration with computer vision, GPU systems, LLM infrastructure, and defense deployment teams. The backend engineer will build resilient infrastructure engineered for failure tolerance and high reliability in secure environments where performance is mission-critical.

Qualifications

Strong backend systems engineering experience in Python, Go, C++, JavaScript/TypeScript, or similar systems-oriented languages

• Experience building real-time or near-real-time data pipelines for video, sensor, telemetry, event, or high-throughput data streams
• Hands-on experience with AI/ML infrastructure, including model deployment, inference pipelines, GPU-backed workloads, or high-performance compute systems
• Familiarity with computer vision or multimodal AI systems, including object detection, tracking, embeddings, video understanding, or event-based reasoning
• Experience with video ingestion and media-processing technologies such as RTSP, GStreamer, WebRTC, FFmpeg, OpenCV, or similar tools
• Ability to design, build, and debug distributed, fault-tolerant backend services and microservice architectures
• Experience deploying production systems across cloud, edge, on-prem, or hybrid environments
• Strong working knowledge of Docker, Kubernetes or container orchestration, CI/CD pipelines, and production reliability practices
• Experience with graph databases, vector databases, relational databases, caching systems, search infrastructure, or temporal/event stores
• Familiarity with data models involving entities, objects, tracks, embeddings, events, timelines, relationships, and system state
• Strong debugging ability across backend services, infrastructure, model pipelines, APIs, and frontend integration points
• Understanding of security-first architecture, especially for restricted-network, compliance-sensitive, on-prem, or air-gapped environments
• Comfortable working in an early-stage environment where the system is evolving quickly and requires hands-on implementation, experimentation, and iteration
• Prior experience in defense, robotics, aerospace, physical security, industrial systems, public safety, video intelligence, or mission-critical software strongly preferred

Compensation

Early Stage Startup – Competitive Equity Compensation