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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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.