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Design, build, and operate scalable backend services, APIs, and AI agents driven by large and small language models (LLMs and SLMs), engineered for production reliability from day one. Architect and administer databases that power AI functionality at scale, designing efficient, performant data models that hold up under real-world load. Integrate external APIs and services to embed AI capabilities into existing business systems and workflows without compromising stability or performance. Establish comprehensive automated testing across unit, functional, integration, and performance layers to guarantee production readiness. Build and maintain robust CI/CD pipelines using DevOps and MLOps practices to deploy, monitor, and evaluate applications and services in live production environments. Diagnose and resolve bugs and field issues quickly, and produce clear documentation as needed to support customers and business partners. Partner with product managers and AI engineers to deliver complete, production-quality solutions. Minimum Qualifications and Experience Advanced degree (BS/MS) in Computer Science or equivalent technical discipline. We prioritise tangible impact over tenure; you should demonstrate clear ownership of production-grade AI/ML or backend services through professional work, open-source contributions, or sophisticated personal engineering projects. Required Expertise Strong grounding in computer science fundamentals such as data structures, algorithms, programming, design patterns, virtualization, and so on, paired with sharp problem-solving abilities. Commitment to high code-quality standards (clean, well-documented, modular, maintainable, reliable, efficient, secure coding) and automated testing that stands up to production demands. Practical experience with backend frameworks such as Node.js, Django, Flask, and FastAPI. Proven experience building and scaling large-scale microservices and applications on cloud platforms (e.g., AWS, GCP, Azure) using containerization and orchestration tools (e.g., Docker, Kubernetes). Familiarity with SQL and NoSQL databases, including the ability to write complex queries and optimize database performance at scale. Collaborative team player with strong interpersonal skills and the ability to work effectively alongside remote teammates. Proactive, go-getter mindset and the ability to thrive in a fast-paced startup environment. Preferred Skills Experience with or working knowledge of any of the following is a strong plus: Building AI/ML applications such as AI agents, chatbots, and automation workflows. Vector databases, embeddings, and retrieval-augmented generation (RAG). Model Context Protocol (MCP), Agent-to-Agent (A2A), and Agent Communication Protocol (ACP). AI/ML tools such as PyTorch, TensorFlow, LangChain, LlamaIndex, and similar. .
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.