100ms Agentic AI Engineer interview questions & guide 2026

The following questions reflect the core competencies required for this role. While specific technical challenges may vary depending on the team, you should focus on demonstrating both deep technical intuition and a clear, structured approach to problem-solving.

Technical and Domain Expertise

These questions assess your understanding of LLMs, agentic frameworks, and the specific challenges of building AI in regulated industries like healthcare.

• How would you design an agentic workflow to handle asynchronous medical data retrieval?
• What strategies would you employ to minimize hallucinations in a clinical decision-support agent?

Success at 100ms requires a blend of deep technical rigor and an product-first mindset. You should prepare to articulate not just "how" you build, but "why" your design is the most effective solution for the specific constraints of the healthcare domain.

Technical Depth – You must demonstrate a mastery of current agentic frameworks and LLM orchestration. Interviewers look for evidence that you understand the underlying mechanics of prompt chaining, tool-use, and model fine-tuning.

Product-Centric Engineering – At 100ms, technology is always a means to an end. You must show that you can translate user requirements into technical specifications and that you consider the impact of your code on the end-user experience.

Security and Compliance Awareness – Given the focus on healthcare, you will be evaluated on your ability to build "secure-by-design" systems. Be prepared to discuss data residency, PII handling, and ethical AI implementation.

The interview process at 100ms is designed to be rigorous, collaborative, and focused on practical application. You can expect a series of discussions that move from initial technical screens to deep-dive architecture sessions and behavioral assessments. The pace is generally fast, reflecting the company’s agile culture, and you will likely interact with both engineering leads and product managers to ensure alignment across disciplines.

This timeline provides a high-level view of the candidate journey from initial screening to the final decision. You should use this to pace your study, ensuring you have refreshed your architectural design fundamentals before the later-stage deep dives. Note that timelines can shift based on team availability, so maintain flexibility in your scheduling.

AI Agent Architecture

This area tests your ability to design systems where agents manage complex, multi-step tasks. You will be evaluated on your ability to handle state, tool integration, and error recovery.

Be ready to go over:

• Orchestration frameworks – Approaches to managing agent loops and tool selection.
• Latency optimization – Techniques to keep agent response times within real-time thresholds.

As an Agentic AI Engineer, your primary responsibility is to build the intelligence layer of the 100ms platform. You will develop and maintain the agentic frameworks that allow our software to perform autonomous tasks, such as transcribing, summarizing, or analyzing live interactions in clinical settings.

You will collaborate daily with product teams to define the capabilities of these agents and with infrastructure engineers to ensure that the AI models are deployed in a scalable and cost-effective manner. Your work will directly impact the efficiency of healthcare providers by reducing administrative burdens and providing real-time, actionable insights during patient consultations.

A successful candidate for the Agentic AI Engineer role at 100ms will have a strong foundation in both software engineering and machine learning.

• Must-have skills – Proficiency in Python, experience with LLM orchestration (e.g., LangChain, LlamaIndex), and a solid understanding of vector databases and RAG (Retrieval-Augmented Generation).
• Nice-to-have skills – Experience with real-time streaming data, familiarity with healthcare data standards (HL7/FHIR), and prior experience deploying production-grade AI agents.
• Soft skills – Strong communication skills are essential, as you will be translating complex AI capabilities into product features that non-technical stakeholders can understand and trust.

Q: What is the typical timeline from the first screen to an offer? A: While it varies by candidate and team, most processes move through the stages within a 2-to-4-week window. Being prepared and responsive will help ensure you stay on the faster end of that spectrum.

Q: How technical are the interviews? A: Expect a high level of technical rigor. You will be asked to whiteboard architectures and discuss the trade-offs of specific AI implementation strategies in detail.

Q: Does the role require prior healthcare experience? A: It is not strictly mandatory, but you must demonstrate a strong willingness to learn the regulatory and safety requirements of the industry.

Q: Is there a coding component to the interview? A: Yes, you should expect to demonstrate your ability to write clean, production-ready code, particularly regarding agent logic and API integrations.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses focused and impactful.
• Think aloud: During system design rounds, articulate your thought process clearly. Interviewers are often more interested in your problem-solving logic than in finding a single "correct" answer.
• Stay current: Be prepared to discuss the latest advancements in AI agent research, as the field is moving rapidly and 100ms values engineers who stay ahead of the curve.
• Ask questions: At the end of your interviews, ask insightful questions about the team’s current roadmap or the biggest technical challenges they are facing.

The Agentic AI Engineer position at 100ms offers a unique opportunity to shape the future of real-time healthcare communication. By focusing on your core technical strengths, architectural intuition, and ability to navigate complex domain requirements, you will be well-positioned to succeed. Remember that candidates can explore additional interview insights, practice questions, and preparation resources on Dataford.

The compensation data provided covers a broad range, reflecting the variance in seniority and location for this role. Candidates should interpret these figures as market-aligned benchmarks that may include base salary, performance bonuses, and equity components, depending on the specific level and offer package.