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.