Senior AI Engineer

FinTop Consulting; Malaysia; On-site; MYR 180,000 - 260,000; Full time

Job summary

FinTop Consulting seeks an experienced AI software engineer to lead production-grade LLM-powered applications. You will design agents, build back-end services and interfaces, and own cloud deployments. You will integrate AI across CRM, support, and analytics platforms, while creating evaluation frameworks and upholding AI safety standards.

The role requires strong full-stack capabilities, DevOps expertise, and excellent communication to explain technical decisions to non-technical stakeholders.

Qualifications

• 2+ years hands-on experience developing LLM-powered applications.
• 5+ years professional software engineering experience.
• Strong full-stack engineering skills: backend services, APIs, and frontend.
• DevOps expertise with CI/CD pipelines, containerization, and cloud deployments.
• Experience embedding AI into existing business applications.
• Excellent English communication skills.
• Knowledge of prompt engineering, RAG, multi-agent orchestration.

Responsibilities

• Take AI use cases from proof of concept through production.
• Design and develop LLM-powered applications and agents.
• Build full-stack technology including back-end services and frontend.
• Own deployment and operations on the chosen cloud platform.
• Integrate AI into CRM, ticketing, and other platforms.
• Develop evaluation frameworks and measure business impact.
• Establish AI safety standards and data handling practices.
• Collaborate with Head of AI Transformation on architecture and effort estimates.

Skills

Production AI deployments

Software engineering 5+ years

Full-stack engineering

DevOps pipelines

LLM app ecosystem

APIs & backend

Failure modes in agentic systems

AI integration in business apps

Independent work

English communication

Tools

AWS

GCP

Azure

Job description

Job Duties

• Take high-priority AI use cases from proof of concept through to production, owning the entire lifecycle. Key areas include customer support, sales enablement, compliance and KYC processes, marketing content, internal operations, and analytics assistants.
• Design and develop the LLM-powered applications and agents that support these use cases, including RAG pipelines, agentic workflows, tool and function calling, structured outputs, and multi-agent orchestration, leveraging frontier model APIs such as Anthropic Claude, OpenAI, and others.
• Build the full-stack technology surrounding the AI solutions, including backend services, APIs, and the front-end experiences that users interact with directly.
• Take ownership of deployment and ongoing operations by deploying solutions to the chosen cloud platform, monitoring performance, and ensuring reliable day-to-day operation.
• Integrate AI into the platforms and systems where business activity takes place, including CRM systems, ticketing and customer support platforms, communication tools, internal databases, and trading-related back-office systems.
• Develop evaluation frameworks and establish quality metrics tailored to each use case. Run evaluations before and after changes and instrument solutions to demonstrate measurable business impact with supporting evidence.
• Help establish company-wide standards for AI safety, data handling, and guardrails, and ensure solutions are built in accordance with them. Work with sensitive customer and financial data, address risks such as prompt injection and data leakage, and design for traceability so AI-generated actions and outputs can be logged, audited, and explained within a regulated financial services environment.
• Work closely with the Head of AI Transformation on product architecture, infrastructure, and effort estimates during use case assessment, helping determine what can realistically be built today, what may be fragile, and what the true costs and timelines are likely to be.

What we're looking for

• Demonstrated experience taking AI use cases into production—not just prototypes, demos, or notebooks. You should be able to clearly explain 2–3 real-world implementations, including the business problem, what you built, what went wrong, and the measurable impact.
• 5+ years of professional software engineering experience, with around 2 years of hands‑on experience developing LLM-powered applications.
• Strong full‑stack engineering skills, with the ability to build backend services and APIs as well as enough front‑end functionality to deliver a practical, user‑friendly interface.
• Strong DevOps expertise, including experience with CI/CD pipelines, containerization, and deploying applications on major cloud platforms such as AWS, GCP, or Azure. Comfortable managing deployments, monitoring applications, and maintaining the systems you build in production.
• Strong hands‑on knowledge of the modern LLM application ecosystem, including prompt engineering, RAG, multi‑agent and sub‑agent orchestration, tool and function calling, and evaluation frameworks.
• Strong software engineering fundamentals across APIs, backend services, data management, deployment, and monitoring. We are flexible about your preferred languages and frameworks; what matters is that you understand the underlying components and can make pragmatic technology choices.
• Clear understanding of common failure modes in agentic systems, including context and token‑window management, plausible but incorrect model outputs, state loss between sessions, and agents silently drifting away from their intended plan. You should also understand the engineering techniques used to identify, prevent, and contain these issues.
• Experience embedding AI into existing business applications, systems, and workflows rather than building only standalone AI applications.
• Ability to work independently with a high degree of ownership, turning ambiguous requirements into working software with minimal direction.
• Excellent English communication skills, with the ability to explain technical decisions, trade-offs, and implications clearly to non‑technical stakeholders.

Nice to have:

• Experience working with data pipelines, including ETL/ELT processes, workflow orchestration, and data warehouse or lakehouse environments.
• Professional experience in fintech, brokerage, banking, or another highly regulated industry.
• Experience developing and deploying voice AI solutions, multilingual applications, or customer‑facing chatbots that operate at scale.