Senior Software Engineer (AI) at Hydron Holdings

Hydron Holdings; Kuala Lumpur; On-site; MYR 180,000 - 240,000; Full time

Job summary

Hydron Holdings in Kuala Lumpur, Malaysia, is hiring a Senior Software Engineer (AI) to design, build, and ship AI-powered products across our FinTech portfolio, including eKYC and fraud detection. This hands-on senior role combines architecture ownership with technical leadership in a regulated environment.

You will work on AWS serverless systems, large language models, and AI observability while mentoring teammates and driving architectural decisions aligned with product goals.

Qualifications

• 3+ years backend or full-stack engineering with 2+ years AI/ML production experience.
• Strong Python skills with NoSQL, REST APIs, and AWS serverless architecture.
• Experience building LLM-based solutions: prompt engineering, agent-style architectures, and integration with providers such as AWS Bedrock, OpenAI, or Anthropic.
• Experience evaluating and monitoring AI systems—automated testing, quality metrics, and production observability.
• Proven track record designing scalable APIs and distributed systems.
• Understanding of applied ML integration (OCR, biometrics, document validation, or fraud/anomaly detection).
• Excellent problem-solving and communication skills; able to explain complex technical concepts to non-technical stakeholders.
• A team player in an Agile environment, balancing hands-on coding with technical leadership.
• Active interest in AI advancements and industry best practices.

Responsibilities

• Design and implement end-to-end AI systems — inference pipelines, agent workflows, and tool-calling architectures.
• Build LLM-powered features on AWS Bedrock: context orchestration, system prompts, memory, retrieval (RAG).
• Develop and optimize ML components for identity verification and fraud detection.
• Build, scale, and secure microservices on AWS serverless (Lambda, API Gateway, DynamoDB, S3, Bedrock).
• Own evaluation frameworks, guardrails, and AI observability — logging, tracing, and latency/cost-aware fallback strategies.
• Oversee model deployment and performance monitoring for ML and LLM components in production.
• Contribute to the technical roadmap; drive architecture decisions aligned with product and compliance goals.
• Evaluate and integrate third-party services — verification vendors, OCR engines, biometric SDKs, and LLM providers.
• Collaborate with Product Owner, BAs, UX/UI designers, and QA to deliver seamless product experiences.
• Mentor engineers through code review and CI/CD improvements; promote observability and cost efficiency.

Skills

Python

AWS Serverless

LLM systems

REST APIs

NoSQL databases

Distributed systems

Problem solving

Agile

Tools

Docker

Job description

Apply for Senior Software Engineer (AI) at Hydron Holdings in Kuala Lumpur, M14, MY. This Full time on site position offers great opportunities for career growth.

Job Scope

We are seeking a hands-on Senior Software Engineer to design, build, and ship AI-powered products across our portfolio — including eKYC identity verification, fraud detection, and AI agent chatbots. This is a senior individual-contributor role with strong technical leadership: you will own architecture decisions, set the engineering bar, and mentor teammates, while spending most of your time building. You will work in a small, talented squad creating systems that combine AWS serverless technology, large language models, and machine learning within a regulated FinTech environment.

Responsibilities

• Design and implement end-to-end AI systems — inference pipelines, agent workflows, and tool-calling architectures — from design through production readiness.
• Build LLM-powered features on AWS Bedrock: context orchestration, system prompts, memory, retrieval (RAG), and structured inputs/outputs.
• Develop and optimize ML components for identity verification and fraud detection (OCR, face matching, anomaly and spoof detection).
• Build, scale, and secure microservices on AWS serverless (Lambda, API Gateway, DynamoDB, S3, Bedrock).
• Own evaluation frameworks, guardrails, and AI observability — logging, tracing, quality monitoring, and latency/cost-aware fallback strategies across models and providers.
• Oversee model deployment and performance monitoring for ML and LLM components in production.
• Contribute to the technical roadmap; drive architecture decisions aligned with product and compliance goals.
• Evaluate and integrate third-party services — verification vendors, OCR engines, biometric SDKs, and LLM providers — as needed.
• Collaborate with Product Owner, BAs, UX/UI designers, and QA to deliver seamless product experiences.
• Mentor engineers through code review, pairing, and enforcing development best practices; drive continuous improvement in CI/CD, observability, and cost efficiency.
• Ensure compliance with PDPA, PCI DSS, and other relevant standards, including responsible AI practices.

Requirements

• 3+ years of backend or full-stack engineering experience, including 2+ years hands‑on work with AI/ML or LLM systems in production.
• Strong Python skills, with experience in NoSQL databases, REST APIs, and AWS serverless architecture.
• Demonstrated experience building LLM-based solutions: prompt engineering, agent‑style architectures, and integration with providers such as AWS Bedrock, OpenAI, or Anthropic.
• Experience evaluating and monitoring AI systems — automated testing, quality metrics, and production observability.
• Proven track record designing scalable APIs and distributed systems.
• Understanding of applied ML integration (OCR, biometrics, document validation, or fraud/anomaly detection).
• Excellent problem‑solving skills; able to translate technical decisions into business impact.
• Strong written and verbal communication skills, capable of explaining complex technical concepts to non‑technical stakeholders.
• A team player who thrives in a collaborative, idea‑driven, Agile environment, balancing hands‑on coding with technical leadership.
• Actively follows AI advancements and industry best practices.

Nice to have

• Experience building or maintaining Retrieval‑Augmented Generation (RAG) systems, including vector databases and embedding pipelines.
• Familiarity with AI guardrails, model safety measures, or responsible AI best practices.
• Familiarity with KYC/eKYC, FinTech, or RegTech domains.
• Experience fine‑tuning LLMs or building automated AI training pipelines.
• Node.js, Docker, or containerized deployment experience.