Senior Machine Learning Engineer

Status Neo is seeking a Senior Machine Learning Engineer to lead the development and deployment of advanced AI models. In this role, you will be responsible for the end-to-end lifecycle of our machine learning systems from architectural design and data preprocessing to model training, optimisation, and production deployment.

You will work at the intersection of generative AI and traditional machine learning, building the engines that power two flagship initiatives: (automated requirements engineering via LLMs) and Intelligent Supply Chain (predictive risk scoring and demand forecasting). Operating within a structured "Sprint Zero" to "Stage Gate" delivery model, you will ensure our models are not just accurate but also robust, explainable, and deployable within strict defence-grade security environments.

The candidate will have responsibilities across the following functions:

• Regulation Parsing:
  Design and fine-tune Large Language Model (LLM) pipelines to interpret complex regulatory texts (e. g., military standards, building codes) and extract structured rules.
• Rule Formalisation:
  Convert natural language requirements into computer-processable formats (e. g., logic tuples) that can be executed by downstream compliance engines.
• Semantic Search:
  Implement RAG (Retrieval-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data.
• Prompt Engineering: optimise prompt strategies (few-shot learning, chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining.

• Forecasting Engines:
  Develop time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals.
• Risk Scoring:
  Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.
• Optimisation Algorithms:
  Design algorithms for multi-objective optimisation (e. g., balancing cost vs. lead time vs. risk) to support procurement decision-making.

• Model Deployment:
  Containerise models using Docker/Kubernetes and deploy them into secure, on-premise inference environments.
• Pipeline Orchestration:
  Build automated training and inference pipelines using tools like Kubeflow or MLflow to ensure reproducibility and scalability.
• Performance Optimisation:
  Optimise model inference latency and resource usage (e. g., quantisation, distillation) to run efficiently on available hardware.
• Monitoring and retraining:
  Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement.

• Experience:
  
  5+ years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments.
• Domain Adaptability:
  Ability to quickly learn and apply ML techniques to specialised domains like defence engineering, supply chain, or construction.
• Structured Delivery:
  Experience working in agile environments (Sprints) while adhering to rigorous engineering standards and documentation requirements.
• Collaboration:
  
  Strong communication skills to work effectively with Data Scientists, Backend Engineers, and Domain Experts to align technical solutions with business needs.

• Core ML/AI:
  Expert proficiency in Python and standard ML libraries (PyTorch, Tensor Flow, Scikit-learn, Pandas, Num Py).
• NLP and GenAI:
  Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, Lang Chain).
• MLOps:
  Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).
• Data Handling:
  Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data.
• Algorithm Design:
  Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.