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- Lead innovation initiatives and exploration of GenAI technologies
- Stay abreast of the latest developments in generative AI research and contribute to Proof-of-Concept projects
- Democratisation of GenAI
- Drive and promote the democratisation of GenAI across the organization
- Develop and promote standards to ensure consistency and quality in GenAI implementations
- Support in building and nurturing a GenAI Center of Excellence (CoE)
- Identify capabilities and provide guidance, mentor, training, and problem-solving assistance
- Enable business units and functions in leveraging GenAI capabilities effectively
- AI Design and Development, and Maintenance:
- Design AI solutions for business use cases, encompassing both existing and new systems and processes
- Design and implementation of generative AI and machine learning models
- Running AI and ML Ops for IT-managed applications
- Support in managing vendors to achieve defined delivery and operations objectives
- Optimise generative models & prompts for performance, scalability, and efficiency
- Experiment with advanced generative algorithms such as GANs, VAEs, MoE (Mixture of Experts), Large-language Model (LLM) Merging and others
- Analyse and pre-process large datasets to uncover patterns relevant to Generative AI Modelling
- Handling different modalities of data including unstructured text, image, audio and video
- Analyse the model response and track the LLM model drift and data drift for optimal retraining of GenAI models
- Contribute to data quality and feature engineering processes to enhance model performance
- Deploy generative models into production environments
- Collaborate with software engineer for seamless integration into applications and systems
- Stay updated with the latest advancements in AI, GenAI and machine learning
- Contribute to projects to push the boundaries of GenAI technologies
- Bachelor's degree in IT, Computer Science, AI / ML Engineering, Business Analytics or equivalent
- Minimum of 10 years of professional experience in the data engineering and analytics field, including 5 years in Data Science / AI / Machine Learning engineering with a focus on Generative AI technologies
- Proficiency in programming languages such as Python,Rust and SQL
- Expertise in Generative AI frameworks and libraries, e.g., LangChain, LlamaIndex, AutoGen, CrewAI, Langraph, llamacpp & OpenAI.
- Expertise in Data science and machine learning frameworks and libraries, such as Scikit-learn, TensorFlow, and PyTorch.
- Strong understanding of data pre-processing, feature engineering, and model evaluation techniques.
- Good knowledge on Prompt engineering techniques and cost optimization strategies for Closed Source LLMs.
- Experience in fine-tuning open-source Large Language Models (LLMs) such as Llama 2, Gemma, Mixtral, Phi-2, Orca2,function calling LLMs etc.
- Proficiency in developing LLM-based RAG (Retrieval Augmented Generation)
- Familiarity with cloud-based AI services and platforms, with a preference for Azure.
- Good knowledge of Multimodal language models, embeddings, including Llava, GPT-4V, and Gemini.
- Experience working with closed-source Large Language Models, e.g., OpenAI, Gemini & mistral.
- Good understanding of embedding models and embedding pipelines.
- Supports specific types of AI applications pipelines with Vector databases & Graph databases.
- Ability to develop LLMbased agentic workflows to support suitable business use cases.
- Experience with Git and Agile software project management tools, such as JIRA, Confluence, and CI/CD pipelines.
- Strong experience in converting business requirement into technical requirement and providing end to end solution flow for AI applications
- Lead innovation initiatives and exploration of GenAI technologies
- Stay abreast of the latest developments in generative AI research and contribute to Proof-of-Concept projects
- Democratisation of GenAI
- Drive and promote the democratisation of GenAI across the organization
- Develop and promote standards to ensure consistency and quality in GenAI implementations
- Support in building and nurturing a GenAI Center of Excellence (CoE)
- Identify capabilities and provide guidance, mentor, training, and problem-solving assistance
- Enable business units and functions in leveraging GenAI capabilities effectively
- AI Design and Development, and Maintenance:
- Design AI solutions for business use cases, encompassing both existing and new systems and processes
- Design and implementation of generative AI and machine learning models
- Running AI and ML Ops for IT-managed applications
- Support in managing vendors to achieve defined delivery and operations objectives
- Optimise generative models & prompts for performance, scalability, and efficiency
- Experiment with advanced generative algorithms such as GANs, VAEs, MoE (Mixture of Experts), Large-language Model (LLM) Merging and others
- Analyse and pre-process large datasets to uncover patterns relevant to Generative AI Modelling
- Handling different modalities of data including unstructured text, image, audio and video
- Analyse the model response and track the LLM model drift and data drift for optimal retraining of GenAI models
- Contribute to data quality and feature engineering processes to enhance model performance
- Deploy generative models into production environments
- Collaborate with software engineer for seamless integration into applications and systems
- Stay updated with the latest advancements in AI, GenAI and machine learning
- Contribute to projects to push the boundaries of GenAI technologies
- Bachelor's degree in IT, Computer Science, AI / ML Engineering, Business Analytics or equivalent
- Minimum of 10 years of professional experience in the data engineering and analytics field, including 5 years in Data Science / AI / Machine Learning engineering with a focus on Generative AI technologies
- Proficiency in programming languages such as Python,Rust and SQL
- Expertise in Generative AI frameworks and libraries, e.g., LangChain, LlamaIndex, AutoGen, CrewAI, Langraph, llamacpp & OpenAI.
- Expertise in Data science and machine learning frameworks and libraries, such as Scikit-learn, TensorFlow, and PyTorch.
- Strong understanding of data pre-processing, feature engineering, and model evaluation techniques.
- Good knowledge on Prompt engineering techniques and cost optimization strategies for Closed Source LLMs.
- Experience in fine-tuning open-source Large Language Models (LLMs) such as Llama 2, Gemma, Mixtral, Phi-2, Orca2,function calling LLMs etc.
- Proficiency in developing LLM-based RAG (Retrieval Augmented Generation)
- Familiarity with cloud-based AI services and platforms, with a preference for Azure.
- Good knowledge of Multimodal language models, embeddings, including Llava, GPT-4V, and Gemini.
- Experience working with closed-source Large Language Models, e.g., OpenAI, Gemini & mistral.
- Good understanding of embedding models and embedding pipelines.
- Supports specific types of AI applications pipelines with Vector databases & Graph databases.
- Ability to develop LLMbased agentic workflows to support suitable business use cases.
- Experience with Git and Agile software project management tools, such as JIRA, Confluence, and CI/CD pipelines.
- Strong experience in converting business requirement into technical requirement and providing end to end solution flow for AI applications