MLOPS E

GCS Recruitment Specialists; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

GCS Recruitment Specialists is seeking a Senior Machine Learning Engineer to lead end-to-end development of AI models for enterprise-scale applications. You will design architectures, preprocess data, train models, and deploy in secure environments while ensuring robustness and explainability.

Join a role that spans LLMs, predictive analytics, and MLOps, with a focus on productionizing pipelines, monitoring performance, and enabling data-driven decision making across global teams.

Qualifications

• 5+ years of experience in ML engineering or equivalent
• Experience deploying models to production in secure environments
• Strong collaboration with data scientists and engineers
• Proficiency with Python and ML libraries (PyTorch, TensorFlow, scikit-learn)

Responsibilities

• Develop LLM/NLP pipelines to interpret regulatory texts and extract structured rules
• Build forecasting and risk-scoring models for procurement and supply chain contexts
• Design and implement MLOps pipelines for reproducible training and deployment
• Optimize model performance and monitor drift in production environments

Skills

Python

ML Libraries

NLP/GenAI

MLOps

Data preprocessing

Tools

Docker

Kubernetes

MLflow

Kubeflow

Job description

Senior Machine Learning Engineer
Location: Abu Dhabi, UAE - Remote Global
Type: Contract

Role Summary
We are seeking a Senior Machine Learning Engineer to lead the development and deployment of advanced AI models. In this role, you will be responsible for the end-to-end lifecycle of 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 automated requirements engineering via LLMs and predictive risk scoring and demand forecasting solutions. Operating within a structured "Sprint Zero" to "Stage Gate" delivery model, you will ensure models are not just accurate, but also robust, explainable, and deployable within strict security environments.

Key Responsibilities

1. LLM & NLP Pipelines

• 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.

1. Predictive & Analytical Models

• 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.

1. MLOps & Productionization

• Model Deployment: Containerize models using Docker/Kubernetes and deploy them into secure, on-premises 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 complex 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, this role is for you.

GCS is acting as an Employment Business in relation to this vacancy.