Must have

• AI

• Python

• SQL

• GitHub

• Cloud

• Azure

• Stakeholder management

• NLP

• English (C1)

Nice to have

• Docker

• Kubernetes

• GCP

• Cloud platform

• UI

• React

Requirements description

What you need to have to succeed in this role

• Proven experience as a Tech Lead or Senior Engineer in AI/ML projects.
• Deep knowledge of LLM architectures, prompt engineering, and RAG.
• Strong coding skills in Python and working knowledge of SQL, GitHub, and CI/CD processes.
• Experience with cloud environments (Azure / GCP).
• Excellent leadership, communication, and stakeholder management skills can present technical solutions, capabilities, considerations, and features to business parties.
• Ability to manage multiple priorities and technology work for building NLP, ML & AI solutions experience in experimenting and developing with LLM.
• Follow AI best practices, ensuring fairness, transparency and accountability in AI model and system are followed by the team.

Offer description

Some careers shine brighter than others.

If you’re looking for a career that will help you stand out, join HSBC, and fulfil your potential. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further.

Your career opportunity

We are looking for a highly capable and experienced Tech Lead to guide our AI and Machine Learning initiatives. In this role, you will be responsible for driving the technical direction of LLM-based solutions, overseeing prompt engineering efforts, and ensuring robust, scalable AI systems are delivered. You will lead a cross-functional team, manage project priorities, and act as the main technical point of contact across internal and external stakeholders.

Your responsibilities

1. Lead the architecture and development of AI/ML solutions, with a focus on Large Language Models (LLMs) and prompt engineering.
2. Oversee fine-tuning and deployment of pre-trained models using domain-specific data.
3. Guide the implementation of Retrieval-Augmented Generation (RAG) systems and manage vector database integration.
4. Drive experimentation and innovation in NLP, ML, and AI technologies, ensuring alignment with business goals.
5. Ensure adherence to ethical AI practices, focusing on fairness, transparency, and accountability.
6. Provide hands-on technical leadership and mentoring to engineers and data scientists on the team.

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