Machine Learning Engineer

We are seeking a highly motivated Machine Learning Engineer to design, build, and deploy scalable machine learning solutions that enhance customer experiences and business operations. The ideal candidate will work closely with data scientists, software engineers, and product teams to develop production-grade ML systems capable of handling large-scale datasets.
Responsibilities:
Design, develop, and deploy machine learning models for real-world business applications.
Build scalable ML pipelines for data preprocessing, training, validation, and deployment.
Develop predictive models, recommendation systems, NLP solutions, and classification algorithms.
Collaborate with software engineering teams to integrate ML models into production environments.
Optimise model performance, scalability, and reliability.
Monitor deployed models and improve accuracy through continuous retraining and evaluation.
Work with large-scale structured and unstructured datasets.
Conduct A/B testing and performance analysis of ML solutions.
Implement MLOps best practices for model versioning, deployment, and monitoring.
Stay updated with emerging trends in AI, Machine Learning, and Generative AI technologies.
Requirements:
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field.
2-5 years of experience in Machine Learning Engineering or related roles.
Strong programming skills in Python.
Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Strong understanding of supervised and unsupervised learning algorithms.
Experience with SQL and large-scale data processing.
Knowledge of software engineering principles, data structures, and algorithms.
Experience deploying ML models on cloud platforms such as AWS.
Familiarity with Docker, Kubernetes, and CI/CD pipelines.
Preferred Qualifications:
Experience with Large Language Models (LLMs) and Generative AI.
Knowledge of NLP, Computer Vision, or Recommendation Systems.
Experience with AWS services such as SageMaker, Lambda, S3 EC2 and EKS.
Exposure to MLOps tools such as MLflow, Kubeflow, or Airflow.
Experience handling large-scale distributed systems.
Technical Skills: Python, Machine Learning, Deep Learning, TensorFlow / PyTorch, Scikit-learn, SQL, AWS SageMaker, Docker, Kubernetes, MLOps, Data Engineering Fundamentals, Generative AI / LLMs.

Experience
2-6 yrs