Role overview
We are looking for an AI Engineer who builds things that matter. You will design and ship end-to-end AI solutions for some of APAC's most complex enterprise problems - spanning agentic AI systems, LLM applications, and production ML pipelines. This is a hands-on engineering role embedded within a customer-facing field team, meaning your work will be seen, used, and evaluated by real enterprises from day one.
You will work alongside Kaggle Grandmasters, ML engineers, and domain experts to deliver AI that goes beyond demos - into production, into workflows, and into measurable business outcomes.
This position is based in US.
What you'll work on
Agentic AI & LLM Engineering
- Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step workflows for enterprise customers.
- Develop and deploy LLM-powered applications using techniques including RAG, fine-tuning, prompt engineering, function calling, and tool use.
- Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety.
- Stay current with the rapidly evolving agentic AI landscape - MCP, LLM orchestration frameworks, reasoning models - and bring the best of it into customer engagements.
End-to-End AI Application Development
- Own the full development lifecycle: from problem framing and data exploration through model development, API integration, and production deployment.
- Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows.
- Integrate AI models into customer environments - cloud, on-prem, and hybrid - ensuring performance, stability, and maintainability at scale.
- Develop ML pipelines and LLMOps infrastructure that support continuous model improvement and monitoring in production.
Customer Engagement & Delivery
- Work directly with customer data scientists, engineers, and business stakeholders to translate real-world problems into AI solutions.
- Contribute to pre-sales and proof-of-concept engagements - building fast, credible demonstrations that win technical trust.
- Communicate clearly across audiences: from detailed technical design reviews with engineering teams to outcome-focused updates for business stakeholders.
- Collaborate closely with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver cohesive, high-quality solutions.
What we're looking for
Experience & Background
- 3+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
- Demonstrable experience building LLM-powered applications - RAG pipelines, agentic workflows, fine-tuned models, or similar.
- Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent).
- Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes).
Skills & Capabilities
- Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Solid grounding in classical ML - able to select the right tool for the problem, not just default to the latest LLM.
- Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications.
- Strong problem-solving instincts - comfortable with ambiguity, able to move fast without sacrificing engineering quality.
- Clear communicator who can explain complex AI systems to non-technical stakeholders without oversimplifying.