AI Engineer (Agentic AI)

We are looking for an AI Engineer with strong experience in modern vision and multimodal models , especially on the Nvidia ecosystem , who can also build small services around these models to prove and validate product flows.

The ideal candidate is someone who:

• Understands vision-language models (VLMs) and world foundation models
• Knows how to work with cameras and camera control (PTZ, streams, RTSP, frame handling, etc.)

• Can wrap these capabilities into a simple, robust service to demonstrate end-to-end AI flows in real use cases.

This role is very hands-on and experimental , focused on prototyping, validating ideas quickly, and then hardening the most promising ones.

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Key Responsibilities

• Explore, evaluate, and integrate vision-language models (VLMs) and world foundation models for understanding real-world scenes from cameras.

• Design and run experiments for scene understanding, spatial reasoning, and multimodal interaction (image/video + text).

• Optimize and deploy models on the Nvidia stack (CUDA, TensorRT, DeepStream, or related Nvidia SDKs and tools).

• Work with camera streams (e.g. PTZ, RTSP/IP cameras): frame capture, streaming, basic processing, and latency/performance tuning.

• Prototype AI-driven flows that connect camera input → model inference → actionable output or API.

• Build small backend services or microservices around AI models to test and demonstrate real product flows.

• Expose models through APIs or internal tools so they can be consumed by other teams.

• Write clean, maintainable code with basic testing, monitoring, and logging.

• Collaborate with other engineers and product stakeholders to move from prototype → proof of concept → production-ready solution.

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Must-Have Qualifications

• Strong hands-on experience with computer vision or multimodal AI , especially:

• Vision-language models (e.g. Gemini, GPT-4V, LLaVA, QWIN, or similar)

• Scene understanding, detection, or tracking from camera feeds

• Practical experience with Nvidia software & tools, such as:

• CUDA, TensorRT, DeepStream, Nvidia SDKs / frameworks, or similar

• Experience working with camera systems :

• RTSP or IP cameras, video streams, frame processing, camera configuration/control

• Solid programming skills in at least one of:

• Python
• Ability to design and implement a small service around a model :

• REST/gRPC APIs, background workers, or simple pipelines

• Comfortable working in a fast-paced environment with rapid prototyping and iteration.

Nice-to-Have

• Experience with world models or world foundation models for spatial/scene reasoning, like COSMOS.

• Experience deploying models on edge devices or GPU-based systems.

• Familiarity with containers and infrastructure: Docker, Kubernetes , and a major cloud provider (AWS/GCP/Azure).

Background in robotics, autonomous systems, or real-time perception.