Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases). Experience with model explainability techniques (e.g., SHAP, LIME). Familiarity with reinforcement learning … or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases #J-18808-Ljbffr More ❯
train, validate, and deploy ML solutions on secure, often resource-constrained platforms. Key Responsibilities: Designing and implementing robust ML models suited to real-time or mission-critical defence environments Processing and analysing complex datasets, including geospatial, signals, or operational intelligence data Collaborating closely with software engineers, data scientists, and defence stakeholders to ensure scalable and secure system integration Conducting … from prototype to deployment Understanding of algorithm performance in constrained or sensitive environments Desirable: Prior experience within the defence, aerospace, or national security sectors Familiarity with computer vision, signal processing, or naturallanguageprocessing Exposure to MLOps, edge computing, or synthetic data generation Knowledge of government or MOD procurement and technical frameworks is an advantage If … train, validate, and deploy ML solutions on secure, often resource-constrained platforms. Key Responsibilities: Designing and implementing robust ML models suited to real-time or mission-critical defence environments Processing and analysing complex datasets, including geospatial, signals, or operational intelligence data Collaborating closely with software engineers, data scientists, and defence stakeholders to ensure scalable and secure system integration Conducting More ❯
make a real difference. Optimising & improving performance – Test, refine, and scale AI models to keep everything running smoothly. Research & innovation – Stay ahead of AI trends, explore new models (LLMs, NLP, etc.), and bring fresh ideas to the table. Collaboration & strategy – Work with developers, product managers, and stakeholders to ensure AI solutions align with business goals. Communication & leadership – Break down complex More ❯
