AI71 is seeking a Senior Machine Learning Engineer to lead the development and deployment of advanced AI models for the EDGE Group. 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, optimization, and production deployment.
You will work at the intersection of generative AI and traditional machine learning, building the engines that power two flagship initiatives: LeverEDGE (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 defense-grade security environments.
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.
- 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.
- 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.
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.
Skills Required
- 5+ years of experience in Machine Learning Engineering with production deployments
- Expert proficiency in Python
- Proficiency with ML libraries: PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy
- Strong experience with transformer architectures and NLP frameworks (BERT, GPT, Llama, Hugging Face, LangChain)
- Experience building LLM/NLP pipelines, RAG architectures, and prompt engineering
- MLOps experience: containerization (Docker), orchestration (Kubernetes), experiment tracking and pipeline tools (Kubeflow, MLflow)
- Ability to design data preprocessing pipelines for structured (SQL/tabular) and unstructured (text/PDF) data
- Experience deploying models into secure on-premise inference environments
- Experience developing time-series forecasting, classification/anomaly detection, and optimization algorithms
- Ability to work in agile delivery models and produce rigorous engineering documentation
- Strong communication and cross-functional collaboration skills with data scientists, backend engineers, and domain experts
- Ability to quickly learn and apply ML techniques to specialized domains (defense, supply chain, construction)
What We Do
ai71 builds AI products and delivers strategic advisory services that drive real-world impact, bringing intelligence that works for enterprises and governments.
