Machine Learning Engineer
First seen by Alion on Oct 15, 2026.
About the Role :
We are looking for a Machine Learning Engineer with hands-on experience in developing, training, evaluating, and deploying machine learning models. The ideal candidate will have strong programming skills in Python, a solid understanding of machine learning algorithms, and the ability to translate business problems into scalable, data-driven solutions.
Key Responsibilities :
- Develop, train, evaluate, and optimise machine learning models for business and product use cases.
- Analyse structured and unstructured datasets to identify patterns, extract insights, and develop predictive models.
- Perform data preprocessing, feature engineering, exploratory data analysis, and feature selection to improve model performance.
- Apply appropriate machine learning techniques across classification, regression, clustering, recommendation, and other relevant use cases.
- Evaluate model performance using suitable metrics, validation techniques, and experimentation.
- Build reusable machine learning pipelines for data preparation, model training, evaluation, and inference.
- Deploy machine learning models through APIs and integrate them with applications and backend systems.
- Collaborate with data engineers, software developers, product managers, and business stakeholders to understand requirements and deliver ML solutions.
- Monitor deployed models for performance, data quality, drift, and reliability, and implement improvements as required.
- Follow best practices for code quality, version control, testing, documentation, and reproducibility.
- Contribute to model deployment, automation, and MLOps practices to streamline the machine learning lifecycle.
- Evaluate emerging machine learning techniques and tools to identify opportunities for improving model accuracy, efficiency, and scalability.
Required Skills & Experience :
- 1-5 years of relevant experience in Machine Learning, Data Science, or ML Engineering.
- Strong hands-on proficiency in Python, including libraries such as NumPy, Pandas, and Scikit-learn.
- Good understanding of machine learning algorithms, statistical concepts, model training, and evaluation techniques.
- Experience with data preprocessing, feature engineering, exploratory data analysis, and model optimisation.
- Familiarity with deep learning frameworks such as PyTorch or TensorFlow.
- Experience working with SQL and structured datasets.
- Understanding of model deployment, REST APIs, and integration of ML models into applications.
- Familiarity with Git, software development practices, and collaborative code development.
- Understanding of model monitoring, experiment tracking, and reproducible ML workflows.
- Strong analytical, problem-solving, and communication skills.
Good to Have :
- Experience with cloud-based ML services on AWS, Azure, or GCP.
- Exposure to MLOps tools and platforms for model deployment, monitoring, and lifecycle management.
- Familiarity with Docker, Kubernetes, and CI/CD pipelines.
- Exposure to Generative AI, Large Language Models (LLMs), or Retrieval-Augmented Generation (RAG).
- Experience deploying machine learning solutions in production environments.
Skills
Machine Learning, Python, Data Science, Numpy, Pandas, Scikit-Learn, PyTorch, Tensorflow, Artificial Intelligence, Deep Learning, MLOps