Job Description

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.