Machine Learning Engineer
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Key skills for this role
About the Role
AI71 is seeking a Senior Machine Learning Engineer to lead development and deployment of advanced AI models for the EDGE Group. The role involves end-to-end ML lifecycle, including LLM pipelines, predictive models, and MLOps.
Key Skills for This Role
Responsibilities
- Design and fine tune LLM pipelines to interpret complex regulatory texts and extract structured rules
- Convert natural language requirements into computer processable formats for downstream compliance engines
- Implement RAG architectures for semantic querying of technical documentation
- Optimize prompt strategies (few shot learning, chain of thought) to improve model performance
- Develop time series forecasting models for material demand and spend categories
- Build classification and anomaly detection models for supplier risk assessment
- Design algorithms for multi objective optimization to support procurement decision making
- Containerize models using Docker/Kubernetes and deploy into secure on premise inference environments
- Build automated training and inference pipelines using Kubeflow or MLflow
- Optimize model inference latency and resource usage via quantization, distillation
- Implement monitoring systems to track model drift and performance in production
Requirements
- 5+ years of experience in Machine Learning Engineering with a proven track record of deploying models into production
- Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit learn, Pandas, NumPy)
- Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain)
- Proficiency with MLOps tools and practices including Docker, Kubernetes, and MLflow
- Ability to design data preprocessing pipelines for both structured and unstructured data
- Strong grasp of algorithmic principles for implementing custom logic
- Experience working in agile environments (Sprints) while adhering to rigorous engineering standards
- Strong communication skills to work effectively with cross functional teams
Full Job Posting
Role Summary
- AI71 is seeking a Senior Machine Learning Engineer to lead the development and deployment of advanced AI models for the EDGE Group. 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, a
Key Responsibilities
- LLM & NLP Pipelines (LeverEDGE): Design and fine tune LLM pipelines to interpret complex regulatory texts and extract structured rules
- Convert natural language requirements into computer processable formats (e.g., logic tuples) for downstream compliance engines
- Implement RAG architectures to enable semantic querying of technical documentation and historical project data
- Optimize prompt strategies (few shot learning, chain of thought) to improve model performance on domain specific tasks
- Predictive & Analytical Models (Supply Chain): Develop time series forecasting models to predict material demand and spend categories
- Build classification and anomaly detection models to assess supplier risk profiles
- Design algorithms for multi objective optimization to support procurement decision making
- MLOps & Productionization: Containerize models using Docker/Kubernetes and deploy into secure, on premise inference environments
- Build automated training and inference pipelines using tools like Kubeflow or MLflow
- Optimize model inference latency and resource usage (e.g., quantization, distillation)
- Implement monitoring systems to track model drift and performance in production
Technical Requirements
- Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit learn, Pandas, NumPy)
- Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain)
- Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow)
- Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data
- Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations
Professional Qualifications
- 5+ years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments
- Ability to quickly learn and apply ML techniques to specialized domains like defense engineering, supply chain, or construction
- Experience working in agile environments (Sprints) while adhering to rigorous engineering standards and documentation requirements
- Strong communication skills to work effectively with Data Scientists, Backend Engineers, and Domain Experts
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