About the role
An AI Lead Engineer is responsible for designing, developing, implementing, and managing advanced Artificial Intelligence solutions to address complex business challenges. The role requires strong expertise in Deep Learning, Machine Learning, Generative AI, and AI architecture, along with the ability to lead end-to-end AI development initiatives.
Responsibilities:
- Design and develop scalable AI/ML solutions, ensuring high performance, accuracy, and reliability.
- Lead the development and deployment of Machine Learning and Deep Learning models for real-world applications.
- Architect and implement Generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and other advanced AI technologies.
- Define AI architecture, technical strategies, and best practices for building scalable and production-ready AI systems.
- Evaluate and integrate modern AI frameworks, algorithms, tools, and technologies to improve solution efficiency.
- Collaborate with cross-functional teams, including data scientists, software engineers, product managers, and business stakeholders.
- Mentor junior AI/ML engineers and provide technical guidance throughout the development lifecycle.
- Optimise AI models for accuracy, scalability, performance, cost efficiency, and deployment readiness.
Requirements:
- Candidates should have 6-8 years of hands-on experience in AI/ML engineering, including experience designing AI architectures, developing intelligent applications, deploying production-grade models, and leading technical teams or projects.
- They should demonstrate strong problem-solving abilities, architectural decision-making skills, and the capability to translate business requirements into effective AI-driven solutions.
Core Technical Skills:
- Deep Learning: Neural Networks, CNN, RNN, Transformers, TensorFlow, PyTorch.
- Machine Learning: Supervised and Unsupervised Learning, Model Training, Feature Engineering, Model Optimisation.
- Generative AI: LLMs, Prompt Engineering, RAG, Fine-Tuning, LangChain, LlamaIndex.
- AI Architecture: AI Solution Design, Scalable AI Systems, Microservices, Model Deployment, MLOps.
- Programming: Python, SQL, API Integration, REST APIs.
- Cloud and Deployment: AWS, Azure, or Google Cloud; Docker, Kubernetes, CI/CD.