Machine Learning Engineer

Role Summary

Senior Machine Learning Engineer

LeverEDGE

Intelligent Supply Chain

Key Responsibilities

• **LLM & NLP Pipelines (LeverEDGE)**
• **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 Formalization:**
• 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:**
• optimize prompt strategies (few-shot learning, chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining.
• **Predictive & Analytical Models (Supply Chain)**
• **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.
• **Optimization Algorithms:**
• Design algorithms for multi-objective optimization (e.g., balancing cost vs. lead time vs. risk) to support procurement decision-making.
• **MLOps & Productionization**
• **Model Deployment:**
• Containerize 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 Optimization:**
• Optimize model inference latency and resource usage (e.g., quantization, distillation) to run efficiently on available hardware.
• **Monitoring & retraining:**
• Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement.

Technical Requirements

• **Core ML/AI:**
• Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy).
• **NLP & GenAI:**
• Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain).
• **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.

Professional Qualifications

• **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 specialized domains like defense 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.
• Why This Role?
• You will be building the intelligence that drives critical national infrastructure.
• Your models will not just generate text or predictions; they will directly influence the design of defense systems and the resilience of supply chains.
• If you are ready to apply advanced ML to tangible, high-stakes problems in a rigorous engineering environment, join AI71.