AI and Machine Learning Engineer

An AI/Machine Learning (ML) Engineer designs, builds, and deploys intelligent, self-learning systems that automate processes and generate insights from data. They bridge the gap between data science (model creation) and software engineering (production-level implementation).

Roles and Responsibilities System Design & Development: Design and develop robust, scalable AI and machine learning systems and deep learning applications. Model Implementation: Research, implement, train, and retrain appropriate ML algorithms and models using frameworks like TensorFlow or PyTorch. Data Management: Collaborate with data engineers to build optimized and reliable data pipelines, manage data collection, and perform data preprocessing/feature engineering to ensure data quality. Deployment & Operations (MLOps): Deploy models into production environments, build APIs and microservices for integration with other applications, and manage the infrastructure needed for scaling. Testing & Monitoring: Run tests and experiments to analyse data and fine-tune models for optimal performance; monitor deployed models for performance degradation, bias, or "drift," and implement retraining strategies. Collaboration & Communication: Work closely with data scientists, software engineers, and product managers to translate business problems into ML solutions and communicate complex technical concepts to non-technical stakeholders. Documentation & Ethics: Document workflows, parameters, and results, while ensuring compliance with data governance, security, privacy, and ethical policies. Innovation: Stay updated with the latest AI advancements and research, continuously seeking improvements for existing infrastructure and systems.

Technical Proficiency: Strong programming skills, especially familiarity with other languages like Java or R or Phyton. ML Frameworks/Libraries: Experience with machine learning frameworks and libraries (e.g., scikit-learn, Keras, PyTorch, TensorFlow). Foundational Knowledge: Deep understanding of mathematics, probability, statistics, algorithms, and data structures. Software Engineering Principles: Knowledge of software architecture, system design, and best practices for building production-ready code (including version control, testing). Cloud & MLOps Tools: Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tools (e.g., MLflow, SageMaker, Vertex AI) for deployment and scaling. Soft Skills: Strong analytical, problem-solving, and critical thinking skills, along with excellent communication and teamwork abilities. Education: A Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field is typically required.