Machine Learning Engineer DefendecReconeyez

The Role

Were looking for a Machine Learning Engineer to own and evolve our models and ML infrastructure behind our actor-detection and visual-verification pipeline. This is the team that decides what our cameras see from the object-detection models that flag intrusions to the duplicate-suppression logic that stops a parked car from firing alarms all night to the next generation of vision-language models were bringing in for richer scene understanding (fly-tipping detection license plates image-quality scoring).

This is a hands-on engineering role not a research-only one. Youll train and optimize models and get them running reliably in production building the data pipelines(and MLOps) serving infrastructure and evaluation harnesses that turn a notebook experiment into something that survives contact with real field imagery (day/night IR/RGB weather bad signal). Youll also help shape where we take agentic and LLM/VLM capabilities next.

 What Youll Do

• Train fine-tune and evaluate computer-vision models (object detection image quality static-object/duplicate suppression) on real-world camera imagery
• Own the model-serving pipeline package models into our NVIDIA Triton ensembles (DALI GPU preprocessing TensorRT inference  post-processing) build and deploy TensorRT engines manage the model repository and no-downtime reloads
• Build and curate datasets ingestion labelling and quality control using FiftyOne(Voxel51) and Label Studio; identify and fix the data problems that actually move model accuracy
• Design evaluation harnesses so model changes are measured not guessed regression suites A/B comparisons and metrics tied to real detection quality
• Develop LLM/VLM and agentic capabilities extend our self-hosted VLM/LLM stack(vLLM and similar) build retrieval- and tool-using agents and integrate them into engineering and product workflows

Qualifications :

Must have:

• Strong Python and the modern ML stack PyTorch model training and fine-tuning working in Jupyter / notebook-driven experimentation
• Practical computer vision experience object detection working with image data understanding why models fail in the real world
• Experience taking models to production not just training them model serving optimization and the gap between offline metrics and live behavior
• Self-starter mindset you can take an ambiguous accuracy problem dig into the data run the experiments and ship a measurable improvement independently
• Rigorous about evaluation you care about datasets ground truth edge cases and not fooling yourself with a good-looking number

  Nice to have:

• NVIDIA Triton Inference Server TensorRT DALI or comparable GPU model-serving / optimization experience
• Dataset tooling FiftyOne (Voxel51) Label Studio or similar curation/annotation platforms
• LLM / VLM experience self-hosting (vLLM) fine-tuning (LoRA) RAG or multimodal models
• Agent-building experience tool-using agents MCP or LLM-orchestration frameworks
• MLOps experiment tracking (CometML/Opik or similar) model registries reproducible training pipelines
• Exposure to edge/IoT or resource-constrained inference or to anomaly detection on device telemetry
• Familiarity with NATS / gRPC or other event-driven service communication

Additional Information :

Level

Mid-level (25 years of relevant ML engineering experience). We value an engineer who can both improve a model and keep it running in production over a pure researcher or a pure MLOps specialist depth in the CV/serving stack matters more than breadth across every framework.

Remote Work :

No

Employment Type :

Full-time