Lead AI Engineer (Ref 25949)

Responsibilities

The Lead AI Engineer will collaborate closely with stakeholders and cross-functional teams to identify AI opportunities and design AI solutions to address business use cases, driving data and analytics initiatives with the business. Additionally, the Lead AI Engineer will play a crucial role in developing and promoting standards across the community, driving and promoting the democratisation of GenAI, and supporting in building the GenAI Center of Excellence to enable the respective business units and functions.

Innovation and Exploration:

1. Lead innovation initiatives and exploration of GenAI technologies.
2. Stay abreast of the latest developments in generative AI research and contribute to Proof-of-Concept projects.
3. Drive and promote the democratisation of GenAI across the organization.
4. Develop and promote standards to ensure consistency and quality in GenAI implementations.

Center of Excellence (CoE) Establishment:

1. Support in building and nurturing a GenAI Center of Excellence (CoE).
2. Identify capabilities and provide guidance, mentoring, training, and problem-solving assistance.
3. Enable business units and functions in leveraging GenAI capabilities effectively.

AI Design and Development, and Maintenance:

1. Design AI solutions for business use cases, encompassing both existing and new systems and processes.
2. Design and implement generative AI and machine learning models.
3. Run AI and ML Ops for IT-managed applications.
4. Support in managing vendors to achieve defined delivery and operations objectives.

Model Optimisation and Experimentation:

1. Optimise generative models & prompts for performance, scalability, and efficiency.
2. Experiment with advanced generative algorithms such as GANs, VAEs, MoE (Mixture of Experts), and Large-language Model (LLM) merging.

Data Analysis and Pre-processing:

1. Analyse and pre-process large datasets to uncover patterns relevant to Generative AI Modelling.
2. Handle different modalities of data including unstructured text, image, audio, and video.
3. Analyse the model response and track the LLM model drift and data drift for optimal retraining of GenAI models.
4. Contribute to data quality and feature engineering processes to enhance model performance.

Deployment and Integration:

1. Deploy generative models into production environments.
2. Collaborate with software engineers for seamless integration into applications and systems.

AI Technology Expertise:

1. Stay updated with the latest advancements in AI, GenAI, and machine learning.
2. Contribute to projects to push the boundaries of GenAI technologies.

Requirements

1. Bachelor’s degree in IT, Computer Science, AI / ML Engineering, Business Analytics, or equivalent.
2. 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.
3. Proficiency in programming languages such as Python, Rust, and SQL.
4. Expertise in Generative AI frameworks and libraries, e.g., LangChain, LlamaIndex, AutoGen, CrewAI, Langraph, llamacpp & OpenAI.
5. Expertise in Data science and machine learning frameworks and libraries, such as Scikit-learn, TensorFlow, and PyTorch.
6. Strong understanding of data pre-processing, feature engineering, and model evaluation techniques.
7. Good knowledge of Prompt engineering techniques and cost optimization strategies for Closed Source LLMs.
8. Experience in fine-tuning open-source Large Language Models (LLMs) such as Llama 2, Gemma, Mixtral, Phi-2, Orca2, function calling LLMs, etc.
9. Proficiency in developing LLM-based RAG (Retrieval Augmented Generation).
10. Familiarity with cloud-based AI services and platforms, with a preference for Azure.
11. Good knowledge of Multimodal language models, embeddings, including Llava, GPT-4V, and Gemini.
12. Experience working with closed-source Large Language Models, e.g., OpenAI, Gemini & Mistral.
13. Good understanding of embedding models and embedding pipelines.
14. Support specific types of AI applications pipelines with Vector databases & Graph databases.
15. Ability to develop LLM-based agentic workflows to support suitable business use cases.
16. Experience with Git and Agile software project management tools, such as JIRA, Confluence, and CI/CD pipelines.
17. Strong experience in converting business requirements into technical requirements and providing end-to-end solution flow for AI applications.

Licence no: 12C6060