AI & Automation Team Lead - บริษัท พลังงานบริสุทธิ์ จำกัด (มหาชน)

Responsibilities:

• Technical & Architecture
  AI & GenAI System Development: Design, build, and optimize production-grade AI systems, including Large Language Model (LLM) applications, RAG pipelines, and autonomous AI agents.
  
  
• Team Leadership & Talent Development
  Team Management & Mentorship: Lead, coach, and inspire a multidisciplinary team of AI Automation team , career growth planning, code reviews, and architectural guidance.
  
  
• Project Management & Delivery
  
  End-to-End Project Execution: Own the delivery lifecycle of AI initiatives, including technical scoping, sprint planning, resource allocation, and milestone tracking to ensure on-time, high-quality releases.
  
  Risk & Quality Governance: Proactively identify technical bottlenecks, data limitations, or AI safety risks, implementing mitigation strategies and evaluation frameworks early in the development cycle.
  
  
• Business Strategy & Stakeholder Alignment
  
  Business Impact & ROI: Collaborate with executive stakeholders, product managers, and commercial teams to define the AI roadmap, prioritizing use cases that maximize business value and return on investment (ROI).


Cross-Functional Communication: Translate complex business requirements into practical technical specifications while clearly communicating system capabilities, trade-offs, and progress to non-technical leaders and clients.

Qualifications 
Education & Experience

• Bachelor’s degree or higher in Management, Engineering, Information Technology, or a related discipline.

1. Technical & AI Expertise

• Generative AI & LLMs: Deep understanding of Large Language Model (LLM) architectures, Retrieval-Augmented Generation (RAG), AI Agents, and orchestration frameworks.
• Programming & Frameworks: Expert-level proficiency in Python and core AI libraries including PyTorch, TensorFlow, and Scikit-learn, adhering to clean, modular, and production-grade coding standards.
  

        - MLOps & Cloud Deployment: Strong background in deploying and monitoring models at scale using Docker, Kubernetes, CI/CD pipelines, and cloud platforms (AWS, GCP, or Azure).

2. System & Data Architecture

• End-to-End Architecture: Ability to architect low-latency, high-throughput, and cost-efficient AI systems from prototype to full production.
• Data & Vector Infrastructure: Solid understanding of data pipelines, distributed data processing

3. Leadership & Team Management

• Technical Mentorship: Proven track record of leading, mentoring, and conducting code and architecture reviews for junior and mid-level AI Engineers.
• Project Execution: Skilled in technical scoping, sprint planning, risk mitigation, and driving cross-functional AI initiatives to timely delivery.
  

4. Business Acumen & Communication

• Business-to-AI Translation: Capable of translating complex business problems into pragmatic, high-ROI AI solutions without over-engineering.

   - Stakeholder Alignment: Strong communication skills to articulate technical trade-offs, system limitations, and project impact to non-technical executives and clients.