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About A1 There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time. About the Role As an LLM Application Engineer, you will build the intelligence layer that powers A1's AI experiences. You will work at the intersection of LLMs, software engineering, and product - designing agent workflows, improving model behaviour, and turning AI capabilities into reliable user experiences. You will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation system and continuously improving AI behaviour in production. Focus Build and ship LLM-powered applications and AI agent workflows Design systems for reasoning, planning, memory, tool uuse and multi-step execution Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions Integrate LLMs with APIs, databases, search, internal services, and external tools. Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX Optimise AI systems for quality, latency, and cost Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement Tech Stack Python LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models Agent frameworks and orchestration systems Vector databases and retrieval systems Backend services, APIs, and distributed systems PyTorch / JAX Ideal Experience Strong software engineering fundamentals with experience building AI-powered applications Hands-on experience with LLMs, generative AI, or agent-based systems Experience designing prompts, workflows, evaluations, or AI behaviour Ability to write clean, production-quality code Comfortable working across abstraction layers (model → system → product) Strong problem-solving skills in ambiguous, fast-moving environments Bias toward shipping, iteration, and continuous improvement Outcomes AI features reach production quickly and deliver measurable user impact LLM-powered workflows are reliable, scalable, observable, and maintainable AI quality improves through systematic evaluation, experimentation, and iteration AI workflows become increasingly predictable, efficient, and cost-effective Complex AI capabilities are translated into simple, intuitive user experiences
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.