Artificial Intelligence Engineer

Key Responsibilities

• Plan, design, and oversee AI/ML projects from concept to deployment.
• Define milestones, monitor progress, and ensure timely delivery.
• Build, train, evaluate, and optimize machine learning models across natural language processing, computer vision, and multimodal domains, including LLMs, VLMs, and vision-specific models (e.g., CNNs, ViTs, diffusion-based models).
• Apply a range of techniques such as transfer learning, parameter-efficient fine-tuning, prompt engineering, knowledge distillation, multimodal fusion, and efficient inference methods (quantization, pruning, model compression).
• Work with recent large language models and reasoning-oriented models, applying techniques such as supervised fine-tuning, structured prompting, retrieval-augmented generation (RAG).
• Read and experiment with recent technologies and research papers; evaluate applicability to projects.
• Apply strong foundations in data structures, algorithms, object-oriented programming, and software design patterns to build reliable AI systems.
• Write clean, maintainable, and well-documented code following established team standards. Practice unit/integration testing, CI/CD pipelines, and version control (Git/GitHub).
• Leverage containerization and orchestration tools such as Docker and Kubernetes for reproducible development and deployment.
• Design and consume APIs (REST/GraphQL) for integrating AI models into larger systems.
• Guide junior engineers through technical challenges and project progress.
• Promote knowledge sharing through code reviews, workshops, and documentation.
• Design and manage scalable solutions on AWS, leveraging cloud-native tools and best practices.
• Work with large-scale datalake architectures to support data-driven applications.
• Assist in monitoring and maintaining deployed models and services.
• Communicate technical progress, challenges, and results clearly within the team.
• Contribute to internal documentation and project updates