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Job Description:

The AI Tech Lead provides expert technical leadership to design, build, and operationalize AI solutions that drive measurable business value. The role owns end‑to‑end delivery of AI initiatives and ensures seamless coordination between product, engineering, and cross-functional teams.

Key responsibilities

• Lead and give technical direction, and best practices across GenAI and ML solutions.

• Own end‑to‑end delivery—from design and prototyping to production deployment and optimization.

• Ensure solution quality, security, reliability, and compliance with enterprise standards.

• Collaborate effectively with product owners and application teams to align requirements and manage dependencies.

• Champion technical clarity and communication.
  
  

Preferred skills

• Hands-on experience in Python and Java

• Familiarity with REST APIs and frameworks such as FastAPI , Flask , or similar technologies

• Experience with LLM frameworks including LangChain , LangGraph , and n8n .

• Knowledge of databases such as PostgreSQL and Cosmos DB

• Practical experience with AWS cloud services , especially SageMaker and Bedrock

• Strong knowledge of containerization and orchestration tools such as Docker, Kubernetes
  
  

Qualifications

• University graduate, preferably in IT, Computer Science or related disciplines

• Over 8 years of working experience

• At least 2 years of relevant experience in AI engineering

• Practical experience with AI/MLOps practices in enterprise environments

• Proficiency in both spoken and written English and Chinese
  
  

Responsibilities

Technical Leadership:

• Defines and enforces technical standards, patterns, and best practices across the AI stack

• Ensures alignment with enterprise architecture, security policies, and regulatory requirements.

• Ensures alignment with enterprise architecture, security policies, and regulatory requirements.

• Serves as the SME (Subject Matter Expert) for AI/ML, GenAI, and supporting technologies within the team.

• Provides hands-on guidance in design reviews and code reviews.
  
  

Delivery Ownership (Accountability: End-to-End Delivery & Solution Success):

• Accountable for delivering AI solutions that meet business objectives, not just technical completion.

• Owns the delivery lifecycle—discovery, prototyping, model training/selection, development, integration, testing, deployment, and production.

• Leads backlog technical grooming; estimates complexity, breaks down work, and directs the engineering execution plan.

• Identify potential risks related to technology initiatives and create mitigation strategies
  
  

Knowledge Management:

• Oversee the creation and maintenance of technical documentation

• Maintain an active knowledge base for digital solutions and AI related material and system documents

• Gather user feedback for future improvements

Job Category:

Posting End Date: