Senior Machine Learning Engineer

Overview

Job Summary

Key Responsibilities

• Lead the design and implementation of Large Language Model (LLM) pipelines for automated requirements engineering, focusing on parsing complex regulatory texts such as military standards and building codes to extract structured rules.
• Convert natural language requirements into executable logic tuples and formalized formats for downstream compliance engines, ensuring seamless integration with technical workflows.
• Develop Retrieval-Augmented Generation (RAG) architectures to enable semantic search capabilities across technical documentation and historical project data, enhancing query precision and retrieval efficiency.
• Optimize prompt engineering strategies, including few-shot learning and chain-of-thought techniques, to improve model performance on domain-specific tasks without requiring extensive retraining.
• Design and deploy time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals for accurate supply chain planning.
• Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors, ensuring robust risk mitigation strategies.
• Create multi-objective optimization algorithms to balance critical procurement factors such as cost, lead time, and risk, directly supporting data-driven decision-making in supply chain operations.
• Containerize machine learning models using Docker and Kubernetes, deploying them into secure, on-premise inference environments that meet defense-grade security standards.
• Construct automated training and inference pipelines using Kubeflow or MLflow to ensure reproducibility, scalability, and seamless integration with existing MLOps workflows.
• Optimize model inference latency and resource usage through techniques such as quantization and distillation, ensuring efficient performance across available hardware configurations.
• Implement comprehensive monitoring systems to track model drift and performance degradation in production, establishing feedback loops for continuous improvement and retraining.

Required Qualifications

• 5+ years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments.
• Expert proficiency in Python and standard ML libraries including PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.
• Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks such as Hugging Face and 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.
• Experience working in agile environments (Sprints) while adhering to rigorous engineering standards and documentation requirements.
• Ability to quickly learn and apply ML techniques to specialized domains like defense engineering, supply chain, or construction.
• Strong communication skills to collaborate effectively with Data Scientists, Backend Engineers, and Domain Experts, aligning technical solutions with business needs.

Technical Skills

• Expert proficiency in Python and standard machine learning libraries including PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.
• Strong experience with transformer architectures such as BERT, GPT, and Llama, and proficiency in NLP frameworks like Hugging Face and LangChain.
• Deep understanding of MLOps tools and practices, including containerization with Docker, orchestration with Kubernetes, and experiment tracking with MLflow.
• Ability to design and implement data preprocessing pipelines for both structured data (SQL, tabular formats) and unstructured data (text, PDFs).
• Experience developing and optimizing retrieval-augmented generation (RAG) architectures for semantic search and knowledge retrieval.
• Proficiency in prompt engineering techniques, including few-shot learning and chain-of-thought methodologies, to enhance model performance on domain-specific tasks.
• Strong grasp of algorithmic principles for custom logic implementation, including graph traversal, geometric computations, and multi-objective optimization algorithms.
• Experience deploying machine learning models into production environments using secure, on-premise inference systems and optimizing inference latency through techniques such as quantization and distillation.
• Familiarity with time-series forecasting models and their integration with enterprise resource planning (ERP) systems and external market data.
• Knowledge of classification and anomaly detection models for risk assessment, particularly in domains like financial health, delivery performance, and geopolitical risk scoring.
• Experience building automated training and inference pipelines to ensure reproducibility, scalability, and compliance with rigorous engineering standards.
• Ability to implement monitoring systems for tracking model drift and performance degradation in production environments.

Role Overview

LLM & NLP Pipelines (Platform)

Predictive & Analytical Models (Supply Chain)

MLOps & Productionization

Why Join This Role?