Forward Deployed Engineer

Role Overview

We seek a Forward Deployed Engineer to collaborate directly with various operational companies within the Group. The primary aim is to convert real-life operational challenges into practical and deployable AI-driven solutions. This role involves hands-on engineering duties, engaging closely with business stakeholders to fully understand their workflows, and delivering AI systems from initial concept through production rollout to final handover.

Key Responsibilities

• Partnering with operational units to identify challenges and translate them into actionable AI and automation implementations.
• Conducting comprehensive process analysis, gathering requirements, and interviewing stakeholders.
• Architecting, developing, testing, and launching AI solutions leveraging internal platforms, pre-built models, and reusable components.
• Constructing solutions employing Python, LLM APIs, Retrieval-Augmented Generation (RAG), vector database technologies, AI agent frameworks, and workflow orchestration tools.
• Seamlessly integrating AI solutions with existing operational infrastructures, including APIs, data systems, and business workflows.
• Rapidly prototyping and validating solutions with actual end-users, refining towards stable production releases.
• Developing frameworks to evaluate and measure AI solution performance and quality.
• Monitoring operational deployments to diagnose and resolve issues related to performance, quality, and integration.
• Collaborating with IT, Security, Data & Analytics, and business teams to ensure secure, compliant, and well-architected solutions.
• Documenting technical designs and preparing thorough handoff materials for business owners.
• Training end-users and operational staff to effectively use, support, and extend deployed AI systems.
• Tracking and reporting quantifiable benefits of deployments, including cost reduction, efficiency gains, and license savings.
• Identifying reusable solution components to accelerate future AI adoption within the Group.
• Supporting make-or-buy decisions through technical feasibility demonstrations and cost evaluations of internal developments.
• Leading short-term delivery teams or coordinating contributions from interns when necessary.

Candidate Requirements

• Bachelor’s degree in Artificial Intelligence, Computer Science, Software Engineering, Data Science, Industrial Engineering, or related fields.
• Between 1 and 3 years of direct experience building and launching software or AI applications into production environments.
• Proven track record of deploying practical, user-adopted solutions beyond experimental or prototype stages.
• Recent hands-on experience developing applications utilizing Large Language Models (LLMs).
• Advanced proficiency in Python for both application and data-related development tasks.
• Strong familiarity with LLM APIs, prompt engineering techniques, and context design methodologies.
• Knowledge and application experience with Retrieval-Augmented Generation (RAG) and vector databases.
• Exposure to AI agent frameworks and managing workflow orchestration.
• Experience with system integration, APIs, SQL, and managing data pipelines.
• Understanding of AI evaluation and testing methods.
• Experience deploying and managing software solutions in cloud environments.
• Some familiarity with lightweight front-end development targeting internal tools is a plus.
• Excellent skills in process mapping, business analysis, and stakeholder requirements elicitation.
• Strong ability to translate complex technical ideas to non-technical audiences.
• Direct interaction experience with clients, business divisions, or operational teams is highly desirable.
• Background or exposure to logistics, supply chain, or operationally intensive domains is considered an advantage.

Technical Skills

• Python programming
• Generative AI and Large Language Models (LLMs)
• LLM APIs and Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Vector database management
• AI agent frameworks
• Workflow orchestration tools
• API integration and SQL
• Data pipeline development
• AI evaluation frameworks
• Cloud deployment and management
• Software application development
• Process automation