Machine Learning Engineer - Defendec/Reconeyez at Vosker

Tallinn, , Estonia -

Full Time

Start Date

Immediate

Expiry Date

14 Sep, 26

Salary

0.0

Posted On

16 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, Computer Vision, Object Detection, Model Serving, NVIDIA Triton, TensorRT, DALI, FiftyOne, Label Studio, LLM, VLM, vLLM, MLOps, RAG, Agentic AI

Industry

Software Development

Description

Company Description VOSKER, leading provider of surveillance solutions for remote-area monitoring, is recruiting talent to support its Reconeyez solutions. Every day, we design intelligent, autonomous, solar-powered and cellular-connected surveillance systems for the world’s most demanding environments, providing consumers and businesses with peace of mind and greater knowledge of their world. In a few words, at Reconeyez by VOSKER: you’ll help protect critical assets, work with cutting-edge technology, and grow with a team that thinks big and delivers. Benefits: Fast growing business Fantastic office in Tallinn Down-to-earth, innovative company culture Stebby wellness benefit Additional vacation and health days Job Description The Role We're 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 we're 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. You'll 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). You'll also help shape where we take agentic and LLM/VLM capabilities next. What You'll 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 (2–5+ 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.

Responsibilities

Own and evolve ML models and infrastructure for actor-detection and visual-verification pipelines. This includes training computer vision models, managing the model-serving pipeline via NVIDIA Triton, and developing LLM/VLM capabilities.