Privileged to be supporting one of Asia's leading technology player for a career shoutout as they experienced transformational growth and are seeking to make an impact across the technology sector.
They are actively seeking a team of AI Product Builder / Applied AI Forward Deployed Engineers, ranging from 3 to 10 years of relevant experience. Applications are welcomed from AI Specialists across the world.
(Depending on experience and strengths, successful candidates may focus on reusable AI products and platforms, forward-deployed customer solutions, or both.)
This is a hands-on role for builders who can connect customer needs, business strategy, AI capabilities and engineering execution. You will translate emerging technologies into practical, secure, scalable and commercially relevant products that deliver measurable outcomes.
AI Product Strategy and Discovery
- Identify high-value opportunities across Generative AI, large language models, AI agents, intelligent automation, computer vision, multimodal AI and decision intelligence.
- Engage customers and industry stakeholders to understand unmet needs, operational challenges and technology environments.
- Translate complex problems into product concepts, technical requirements, user journeys and measurable success criteria.
- Validate product-market fit through prototypes, proof-of-concepts, design partnerships and early adopter programmes.
- Shape use cases and roadmaps based on customer evidence, technical feasibility and commercial potential.
Build and Deploy AI Products
- Design, prototype, develop and deploy AI-enabled products, applications, platforms and reusable capabilities.
- Progress successful prototypes into secure, scalable and production-grade solutions.
- Develop AI agents, enterprise assistants, conversational applications, retrieval-augmented generation, semantic search, intelligent document processing, computer vision and workflow automation solutions.
- Integrate AI capabilities with enterprise applications, APIs, data platforms, security controls and operational workflows.
- Balance customer value, functionality, speed, scalability, cost, security and technical complexity.
Forward-Deployed Customer Engineering
- Work directly with customers to diagnose ambiguous and mission-critical business or operational problems.
- Adapt and extend AI products within customer environments, including integration with proprietary data, systems and workflows.
- Collaborate with customer stakeholders from discovery through implementation, testing, launch and adoption.
- Build practical solutions rapidly while maintaining engineering, security and governance standards.
- Turn repeatable deployment patterns into reusable product features, frameworks and accelerators.
Production Engineering and Scalability
- Build modular and reusable AI software components, APIs, backend services and orchestration layers.
- Partner with architecture, cloud, platform and DevOps teams to deliver solutions across cloud, hybrid and enterprise environments.
- Establish automated testing, deployment, evaluation, monitoring and observability practices.
- Monitor application quality, model performance, latency, reliability, availability, usage and cost.
- Apply sound engineering practices, including version control, CI/CD, documentation and code review.
Product Experience, Adoption and Value
- Design AI products that are intuitive, reliable and aligned with real user workflows.
- Define effective interactions across copilots, agents, conversational interfaces, recommendations and decision-support tools.
- Incorporate appropriate human oversight, intervention and escalation.
- Gather user feedback and product analytics to improve usability, adoption and performance.
- Measure outcomes across customer satisfaction, productivity, revenue, cost savings, operational performance and usage.
Responsible AI, Security and Governance
- Embed responsible AI, privacy, cybersecurity, data governance and regulatory considerations throughout the product lifecycle.
- Implement safeguards for hallucination, bias, harmful outputs, data leakage, inappropriate use and model degradation.
- Develop evaluations covering accuracy, relevance, robustness, safety, explainability and user experience.
- Ensure suitable human oversight, transparency, traceability and ongoing monitoring.
- Work with legal, risk, compliance, cybersecurity and data-governance teams to operationalise controls.
Commercialisation and Innovation
- Convert customer-specific solutions into repeatable products, platforms and industry accelerators.
- Contribute to business cases, pricing, packaging, commercial models and go-to-market plans.
- Partner with sales, industry, marketing and solution teams on demonstrations, playbooks and customer success stories.
- Evaluate emerging models, platforms, vendors and frameworks for practical enterprise use.
- Lead experimentation and co-innovation with customers, technology partners, start-ups, universities and research institutions.
Collaboration and Leadership
- Work in multidisciplinary squads involving product managers, AI engineers, software engineers, data scientists, designers, architects and domain specialists.
- Communicate technical concepts clearly to technical and non-technical stakeholders.
- Contribute to product discussions, architecture reviews, technical decisions and code reviews.
- Depending on seniority, own features or workstreams, lead deployments, mentor builders and shape engineering standards.
- Foster customer focus, responsible innovation, engineering excellence and execution discipline.
What We Are Looking For
- Demonstrated experience building and deploying AI, data, software or platform products in enterprise or customer environments.
- Experience with foundation models, large language models, machine learning models or multimodal AI applications.
- Familiarity with AI agents, retrieval-augmented generation, embeddings, vector databases, semantic search, model APIs or orchestration frameworks.
- Understanding of cloud platforms, APIs, databases, data pipelines, containerisation, enterprise integration and production software practices.
- Experience with model evaluation, AI observability, guardrails, responsible AI or production monitoring.
- Ability to translate customer needs into practical product and technical solutions.
- Strong problem-solving, communication and execution capabilities, with commercial awareness of adoption, customer value and scalability.
Candidates are not expected to have experience in every technology or AI domain listed. Appointment level and scope will be calibrated according to capability, technical depth, product-building experience and leadership potential.
What Will Differentiate You
- You have built AI applications or products used by real customers or users.
- You can explain your personal contribution and the impact achieved.
- You understand both AI models and the software systems required to deploy them reliably.
- You can move between customer problems, product decisions and hands-on technical implementation.
- You can demonstrate working products, repositories, prototypes, publications or open-source contributions.
- You combine curiosity about frontier AI with discipline around security, reliability, governance, user trust and cost.
Global Applicants
Applications are welcomed from qualified AI specialists worldwide. International candidates should highlight the products or systems they have built, their individual contribution, production scale, customer impact and relevant portfolio links. Employment arrangements, work authorization and relocation considerations will be assessed according to role requirements and applicable regulations.
Ideal Candidate
You may be an engineer with strong product instincts, a data scientist who has moved into production engineering, a researcher translating advanced AI into practical applications, or a product-minded technologist who enjoys working directly with customers.
What matters is your ability to solve meaningful problems, build reliable technology, learn quickly and convert Artificial Intelligence into products that organisations can adopt and scale.
This is an opportunity to build enterprise AI products with regional and global relevance alongside multidisciplinary technology, product and industry teams.
All profiles are handled with highest level of confidentiality.