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Machine Learning Engineer
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Company Description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer to join their team and help build scalable Artificial Intelligence (AI) and Machine Learning (ML) solutions that power intelligent products, business operations, and customer experiences. This role offers the opportunity to develop, deploy, and optimize production-ready Machine Learning models while working across Artificial Intelligence, MLOps, Predictive Analytics, Natural Language Processing (NLP), Data Engineering, and Intelligent Automation.
You will collaborate with Software Engineers, Data Scientists, Product Managers, Data Engineers, and Cloud Engineering teams to build scalable ML pipelines, automate model deployment, improve production systems, and accelerate AI adoption across the organization. This position is ideal for someone who enjoys solving complex engineering challenges while building reliable, high-performance machine learning systems.
Accountabilities
The Machine Learning Engineer will support the full machine learning lifecycle, from model development to deployment and continuous optimization.
Key responsibilities include:
- Design, develop, train, evaluate, and deploy Machine Learning models for production environments.
- Build scalable ML pipelines and support end-to-end MLOps workflows.
- Write clean, efficient, maintainable, and production-quality Python code.
- Develop data pipelines to prepare, transform, and validate structured and unstructured datasets.
- Deploy, monitor, and optimize Machine Learning models in production environments.
- Collaborate with Data Scientists to productionize Machine Learning solutions.
- Improve model performance through feature engineering, experimentation, and continuous optimization.
- Implement CI/CD workflows and automation for Machine Learning deployment.
- Work closely with Product, Engineering, and Architecture teams to deliver scalable AI solutions.
- Monitor model performance, data quality, and production reliability.
- Document machine learning experiments, technical solutions, and engineering best practices.
- Stay current with emerging technologies in Artificial Intelligence, Machine Learning, Deep Learning, and MLOps.
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Requirements
The ideal candidate is passionate about Machine Learning Engineering, software development, and building scalable AI-powered systems.
Required qualifications and skills include:
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, or a related technical field.
- 1 or 2 years of experience in Machine Learning Engineering, Software Engineering, Data Science, Artificial Intelligence, or MLOps.
- Strong programming skills in Python.
- Experience with SQL and large-scale data processing.
- Experience using Machine Learning libraries such as scikit-learn, Pandas, NumPy, TensorFlow, PyTorch, or similar frameworks.
- Knowledge of classical Machine Learning algorithms, supervised learning, unsupervised learning, and model evaluation techniques.
- Understanding of Deep Learning, Natural Language Processing (NLP), or Computer Vision concepts.
- Experience deploying Machine Learning models into production.
- Familiarity with Git, version control, CI/CD workflows, and software engineering best practices.
- Strong understanding of data structures, algorithms, and object-oriented programming.
- Excellent analytical thinking and problem-solving skills.
- Strong written and verbal English communication skills.
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Preferred qualifications include:
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Familiarity with Docker, Kubernetes, MLflow, Airflow, Kubeflow, or modern MLOps platforms.
- Experience building scalable APIs using FastAPI or Flask.
- Experience with feature engineering, model monitoring, and model optimization.
- Knowledge of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI automation.
- GitHub portfolio, Kaggle competitions, open-source contributions, or personal Machine Learning projects.
Benefits
The role offers an opportunity to contribute to cutting-edge AI initiatives while building scalable Machine Learning systems within a collaborative engineering environment.
Benefits include:
- Opportunity to build production-scale Artificial Intelligence and Machine Learning solutions.
- Exposure to modern MLOps, Cloud Computing, and Data Engineering technologies.
- Collaborative international engineering team.
- Continuous learning and professional development opportunities.
- Career progression within a growing technology organization.
- Competitive compensation and comprehensive benefits package.
Equal Opportunity Statement
Our partner is committed to building an inclusive workplace where diversity, equity, and inclusion are valued. Employment decisions are based on skills, qualifications, experience, and business needs. We welcome applicants from all backgrounds and believe diverse perspectives drive innovation and long-term success.
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