What will your typical day look like?As a Machine Learning Engineer in J-Squared’s FALC-AI division, you will build and deploy the ML and deep learning models behind our AI products, working with computer vision, Large Language Models (LLMs), Vision-Language Models (VLMs), Generative AI, and Agentic AI.

Our work spans edge to cloud, from rugged NVIDIA Jetson and Hailo devices in the field to on-premise GPU servers and AWS. You will take models from research to real-world deployment in demanding industrial environments including Defence, Mining, Manufacturing and Retail.

Specifically, your responsibilities will include:

1. AI Model DevelopmentDesign, train, fine-tune, and evaluate deep learning models for computer vision and/or NLP tasks.
2. Work with multi-modal data, including images, video, text, and sensor signals.
3. Stay current with the latest research and turn promising ideas into working prototypes.

1. LLMs, Generative AI, and Agentic AIBuild LLM and VLM-powered applications including RAG pipelines and Agentic AI systems using tool calling and the Model Context Protocol (MCP).
2. Fine-tune openweight LLMs and VLMs and serve them in production with engines such as vLLM and TensorRT-LLM.
3. Evaluate generative models for accuracy, hallucination, latency, and cost.

1. Optimization and Edge-to-Cloud deploymentDeploy models across edge devices (NVIDIA Jetson, Hailo), GPU servers, and AWS.
2. Optimize models using quantization, pruning, distillation, and conversion workflows.
3. Build high-performance inference pipelines with tools such as NVIDIA Triton and gRPC.

What will your typical day look like?As a Machine Learning Engineer in J-Squared’s FALC-AI division, you will build and deploy the ML and deep learning models behind our AI products, working with computer vision, Large Language Models (LLMs), Vision-Language Models (VLMs), Generative AI, and Agentic AI.

Our work spans edge to cloud, from rugged NVIDIA Jetson and Hailo devices in the field to on-premise GPU servers and AWS. You will take models from research to real-world deployment in demanding industrial environments including Defence, Mining, Manufacturing and Retail.

Specifically, your responsibilities will include:

1. AI Model DevelopmentDesign, train, fine-tune, and evaluate deep learning models for computer vision and/or NLP tasks.
2. Work with multi-modal data, including images, video, text, and sensor signals.
3. Stay current with the latest research and turn promising ideas into working prototypes.

1. LLMs, Generative AI, and Agentic AIBuild LLM and VLM-powered applications including RAG pipelines and Agentic AI systems using tool calling and the Model Context Protocol (MCP).
2. Fine-tune openweight LLMs and VLMs and serve them in production with engines such as vLLM and TensorRT-LLM.
3. Evaluate generative models for accuracy, hallucination, latency, and cost.

1. Optimization and Edge-to-Cloud deploymentDeploy models across edge devices (NVIDIA Jetson, Hailo), GPU servers, and AWS.
2. Optimize models using quantization, pruning, distillation, and conversion workflows.
3. Build high-performance inference pipelines with tools such as NVIDIA Triton and gRPC.