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We are seeking a Software Engineer – Machine Learning to design, develop, and deploy machine learning solutions that drive innovation and improve business processes. The ideal candidate will work closely with data scientists, engineers, and product teams to build scalable ML models, optimize performance, and integrate AI-driven solutions into production systems.

• Design, implement, and optimize machine learning models for real-world applications.
• Develop and maintain scalable ML pipelines for data preprocessing, model training, and deployment.
• Integrate machine learning models into production environments using APIs, cloud services, or edge devices.
• Collaborate with data scientists and engineers to improve model accuracy and efficiency.
• Work with big data technologies to process and analyze large-scale datasets.
• Monitor, debug, and optimize machine learning workflows for performance and reliability.
• Stay up to date with the latest advancements in AI/ML technologies and research.

Education & Experience:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3+ years of experience in software engineering, with at least 1+ year in machine learning development.

Technical Skills:

• Programming Languages: Proficiency in Python, Java, or C++ for ML development.
• Machine Learning Frameworks: Experience with TensorFlow, PyTorch, Scikit-Learn, or XGBoost.
• Data Processing & Storage: Strong knowledge of SQL, Pandas, NumPy, and experience with big data tools like Apache Spark or Hadoop.
• Cloud Platforms: Experience with AWS, Google Cloud, or Azure for deploying ML models.
• MLOps & Deployment: Familiarity with Docker, Kubernetes, MLflow, or TensorFlow Serving.
• Software Engineering Best Practices: Knowledge of version control (Git), CI/CD pipelines, and API development (REST, GraphQL).

Soft Skills:

• Strong problem-solving skills and ability to optimize ML models for production.
• Ability to work in cross-functional teams and collaborate with researchers, engineers, and stakeholders.
• Excellent communication skills to explain ML concepts to both technical and non-technical teams.
• Self-motivated and eager to stay updated with emerging ML trends and technologies.

Preferred Qualifications:

• Experience with edge AI, reinforcement learning, or deep learning architectures.
• Knowledge of automated machine learning (AutoML) and hyperparameter tuning.
• Background in A/B testing, statistical modeling, and data-driven decision-making.
• Experience with Natural Language Processing (NLP) or Computer Vision applications.