🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Were looking for an AI Engineer to help us design, evaluate, and scale our next generation of LLM-powered agents. This role is deeply technical, but also product and customer-oriented: youll build datasets, run evaluations, improve model performance, and ensure our agents deliver real value in real workflows. As an AI Engineer, youll work across Engineering, Product, and Customer teams to continuously improve our agent quality, reliability, and speed. You wont just build agents youll build the systems, metrics, and feedback loops that make those agents better over time. Responsibilities Build, curate, and maintain agentic workflows along with datasets for training and evaluation. Run systematic LLM evaluations, track regressions, and ensure models meet quality bars. Define and implement LLM performance metrics(e.g., correctness, latency, hallucination control, safety). Work closely with Product and Customer-facing teams, experiment with prompting techniques, fine-tuning datasets, retrieval strategies, and model configurations and ensure agent behaviour aligns with user expectations. Develop internal tooling to speed up evaluation, annotation, and iteration cycles. Build automated pipelines for regression checks and model monitoring. Create mechanisms that turn real user interactions into actionable model improvements. Ensure agents behave consistently across large-scale production scenarios. Debug complex system behaviours spanning prompts, tools, APIs, and model responses. To understand real-world use cases and failure modes. Translate customer insights into model needs, data requirements, and product improvements. Tune latency, turn-taking, and conversational naturalness for voice AI systems. Write clean, reliable code in Python or JS for model pipelines, tools, and integrations. Think in systems: from data ingestion to model outputs to user-facing behaviour. Success Looks Like Strong evaluation coverage with clear, actionable metrics. Faster iteration cycles due to improved internal tools and workflows. Measurable improvement in agent accuracy, consistency, and safety. Reliable agent performance in production fewer escalations, fewer regressions. Clear alignment between customer needs and agent capabilities. Smooth collaboration across engineering, product, and customer teams. What Were Looking For 25 years experience in software engineering, with strong fundamentals in building reliable, scalable systems.12 years of experience working on AI-backed products(LLM engineering, evaluations, prompting, or agent development). Hands-on experience with LLMs, prompt engineering, evaluation frameworks, or dataset creation. Strong understanding of how LLMs work: prompting, fine-tuning, evaluation, guardrails, and system design. Solid software engineering skills and comfort with APIs and cloud environments. Experience building internal tools or automation pipelines. Ability to think from the customer backward: empathetic, detail-oriented, and outcome-driven. Excellent communicator who collaborates well across cross-functional teams. Fast learner who loves turning cutting-edge research into practical, dependable systems. Bonus: experience with voice AI, speech technologies, or real-time agent systems. Why This Role Matters In healthcare, accuracy and trust are everything. Patients, providers, and payers rely on our AI agents to handle critical interactions. Deployment Engineers make this possible by turning AI from a raw model into something practical, reliable, and human-friendly. Why 100ms.ai You'll be part of a small team at a fast-growing engineering-first startup. You'll work with engineers across the globe with experience in video at places like Facebook and Hotstar. You can grow as an individual contributor or as a team leader - freedom to set your own goals. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. .
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.