Senior Software Engineer

Job Description

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.

• 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.

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