About the Role
We are looking for a Senior Machine Learning Engineer to lead the development, deployment, and operationalization of advanced AI and machine learning solutions across two high-impact initiatives:
- Automated Requirements Engineering Platform powered by Large Language Models (LLMs)
- Supply Chain Intelligence Platform with predictive risk scoring and demand forecasting.
In this role, you will own the end-to-end machine learning lifecycle — from system architecture and data pipelines to model training, optimization, and production deployment. You will work at the intersection of generative AI and classical machine learning, delivering models that are not only accurate, but also robust, explainable, and production-ready.
The environment follows a structured Sprint Zero Stage Gate delivery model and operates under strict defense-grade security and compliance requirements, making this role ideal for engineers who value engineering rigor and real-world impact.
**Key Responsibilities:
- LLM & NLP Pipelines**
- Design and fine-tune LLM-based pipelines to parse and interpret complex regulatory and technical documentation (e.g. military standards, building codes);
- Transform unstructured natural language requirements into machine-executable formats (e.g. logic tuples, structured rules);
- Implement Retrieval-Augmented Generation (RAG) architectures for semantic search across technical documents and historical project data;
- Optimize prompt engineering strategies (few-shot learning, chain-of-thought, prompt templates) to improve domain-specific performance with minimal retraining.
2. Predictive & Analytical Models (Supply Chain)
- Develop time-series forecasting models for material demand, cost trends, and spend categories;
- Build risk scoring, classification, and anomaly detection models to evaluate supplier reliability and exposure (financial, operational, geopolitical);
- Design multi-objective optimization algorithms to balance cost, lead time, and risk in procurement decision-making.
3. MLOps & Productionization
- Containerize and deploy models using Docker and Kubernetes into secure, on-premise inference environments;
- Build reproducible training and inference pipelines using tools such as MLflow, Kubeflow, or similar;
- Optimize inference performance through quantization, distillation, and efficient model architectures;
- Implement monitoring and retraining workflows to detect model drift and ensure long-term performance in production.
Technical Requirements:
- Python expertise and strong hands-on experience with ML frameworks: PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy;
- Deep understanding of NLP and Generative AI, including transformer architectures (BERT, GPT, LLaMA);
- Experience with Hugging Face, LangChain, or similar NLP/LLM frameworks;
- Solid MLOps experience, including Docker, Kubernetes, experiment tracking, and CI/CD for ML;
- Ability to design data pipelines for structured data (SQL, tabular) and unstructured data (text, PDFs);
- Strong algorithmic thinking with experience implementing custom logic (e.g. graph traversal, optimization, geometric or rule-based computations).
Professional Qualifications:
- 5+ years of experience in Machine Learning Engineering with production-grade deployments;
- Proven ability to adapt ML solutions to complex, highly regulated domains (e.g. defense, supply chain, construction, engineering);
- Experience working in agile delivery models, while maintaining strict engineering standards and documentation discipline;
- Strong collaboration and communication skills, with the ability to work closely with Data Scientists, Backend Engineers, and Domain Experts.