AI Engineer

Responsibilities

• Design, develop, and implement scalable AI and generative AI solutions for enterprise use cases.
• Contribute to AI solution architecture, including the selection of appropriate models, frameworks, APIs, cloud services, and technologies.
• Collaborate with business stakeholders, domain experts, and clients to understand requirements and translate them into practical technical solutions.
• Develop and integrate AI solutions leveraging LLMs, generative AI, AI agents, and open-source models.
• Integrate AI solutions with APIs, enterprise applications, databases, and platforms such as Azure OpenAI and Hugging Face.
• Develop and optimise end-to-end AI pipelines covering data processing, model development, deployment, and monitoring.
• Implement RAG, vector databases, similarity search, and other techniques to improve AI application performance and information retrieval.
• Conduct research, proof-of-concepts, and evaluations of emerging AI technologies for enterprise applications.
• Establish appropriate evaluation methods to assess the quality, accuracy, relevance, and performance of AI solutions.
• Ensure AI solutions adhere to appropriate security, privacy, scalability, and responsible AI practices.

Prerequisites

• Bachelors Degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline.
• Minimum 2–3 years of experience in Data Science, Machine Learning, Artificial Intelligence, or related fields.
• Experience designing, developing, integrating, and deploying AI solutions, including LLMs, generative AI, AI agents, and/or open-source AI models.
• Strong knowledge of machine learning, deep learning, NLP, generative AI, and Transformer-based models such as GPT and BERT.
• Proficiency in Python and experience with AI/ML frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, CrewAI, or AutoGen.
• Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in cloud environments.
• Experience with APIs, databases, vector databases, RAG, embeddings, and AI application integration.
• Understanding of solution architecture principles, system integration, scalability, security, and performance optimisation.
• Knowledge of data engineering practices, including data preparation, cleaning, preprocessing, and pipeline development.
• Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
• Strong communication and stakeholder management skills, with the ability to explain technical concepts to both technical and non-technical audiences.
• Passion for AI innovation and the ability to stay current with emerging AI technologies and industry trends.