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.