Artificial Intelligence Engineer

Role Overview 

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 

Relevant Experience 

• 4+ years of experience in AI/ML model design, training, and deployment in production environments.  
• Proven expertise in building and optimizing models, including LLMs, VLMs, and other deep learning architectures.  
• Exposure to transfer learning, self-supervised learning, multimodal AI systems and domain generalization.  
• Knowledge of retrieval-augmented generation (RAG), diffusion models, or other cutting-edge ML techniques.  
• Strong programming skills in Python with solid knowledge of data structures, algorithms, and software engineering best practices.  
• Hands-on experience with large-scale datalake architectures and distributed data processing  
• Experience with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and MLOps practices (CI/CD, experiment tracking, reproducibility).  
• Strong communication, documentation, and presentation skills, with the ability to work across teams and with external partners.  
• Ability to stay current with emerging AI research and assess applicability of new methods to real-world problems.  

Education Requirements 

• Bachelor's or master's degree in computer science, computer engineering, statistics, or mathematics