Senior AI Engineer
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At a glance
Technologies & skills
Cloud & infrastructure
Other technical skills
What you'll be doing
Hands-on engineering role designing, shipping, and owning AI agents end-to-end. Work on agent architectures, retrieval, evaluation, and production readiness with a strong R&D team. Emphasis on clean engineering, production-quality code, and meaningful AI work at scale.
Role signals
- Technical focus
- AI agents, agentic systems, backend engineering, production deployment
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
This is a hands-on engineering role focused on designing, shipping, and owning AI agents end-to-end.
You’ll work on agent architectures, retrieval, evaluation, and production readiness alongside a strong R&D team that values clean engineering and moving fast without cutting corners.
Tools for the trade
Strong Python engineering (production-quality code)
Experience building LLM-powered applications
Hands-on work with agentic AI systems (planning, tools, memory, multi-step workflows)
Familiarity with LangChain, LangGraph, LlamaIndex, CrewAI or similar frameworks
Retrieval systems experience (embeddings, vector databases, hybrid/grounded RAG)
Evaluation-first mindset (offline datasets, regression tests, monitoring)
LLMOps / AgentOps experience (tracing, cost & latency, reliability)
Backend engineering with APIs & services (FastAPI, Postgres, event/workflow systems)
Production deployment using Docker, Kubernetes, CI/CD
Cloud experience (AWS, Azure, or GCP)
High trust, high ownership, and space to build things properly.
No demo theatre.
No endless POCs.
Just meaningful AI work at scale.
If you’re excited about where applied AI is going and want to be ahead of it let’s talk.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Senior AI Engineer role is a hands-on position focused on designing, building, and deploying production-ready AI agent systems using modern frameworks and cloud infrastructure within a collaborative R&D environment.
- Hands-on AI agent development·Medium
- Production engineering and deployment·High
- Retrieval and evaluation systems·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Recent AI Agent Projects
0–8 minSelect 1-2 relevant projects where you designed and deployed agentic AI systems, and prepare concise stories highlighting your ownership and impact.
Refresh Retrieval and Evaluation Concepts
8–15 minStudy your experience with RAG, embeddings, vector databases, and monitoring techniques to discuss technical details confidently.
Summarize Production Deployment Experience
15–21 minOutline your workflow for deploying AI systems using Docker, Kubernetes, and CI/CD on cloud platforms.
Prepare Questions for the Team
21–26 minDraft thoughtful questions about team challenges, technology choices, and collaboration to demonstrate engagement.
Practice Explaining Framework Trade-offs
26–30 minBe ready to discuss your experience with agentic AI frameworks, including why you chose them and lessons learned.
Talking points
, 5 itemsDesigning and Implementing Agentic AI Systems
You should be ready to discuss your experience architecting and building AI agents, including planning, memory, and multi-step workflows, to demonstrate your ability to handle complex agent behaviors.
Production-Quality Python Code
The role emphasizes strong Python engineering; prepare examples of writing maintainable, scalable, and robust code in production environments.
Retrieval-Augmented Generation (RAG) and Vector Databases
Showcase your experience with retrieval systems, embeddings, and vector databases, as these are core to building effective LLM-powered applications.
Evaluation and Monitoring of AI Systems
Be ready to explain how you approach evaluation, regression testing, and monitoring of AI agents to ensure reliability and performance at scale.
Cloud Deployment and Backend Engineering
Discuss your hands-on experience deploying applications using Docker, Kubernetes, and CI/CD pipelines on AWS, Azure, or GCP, as well as building APIs and backend services.
What to research
, 4 itemsRecent Projects in Agentic AI Systems
Review your hands-on experience designing, building, and deploying AI agents, focusing on end-to-end ownership.
Retrieval Systems and Vector Databases
Refresh your knowledge of retrieval-augmented generation, embeddings, and vector database integration in LLM applications.
Production Deployment with Docker and Kubernetes
Be ready to discuss your deployment workflows, CI/CD pipelines, and experience with cloud platforms (AWS, Azure, GCP).
Evaluation and Monitoring Techniques
Prepare examples of how you have evaluated, tested, and monitored AI systems in production environments.
Questions to ask
, 6 itemsWhat are the main challenges your team faces when deploying AI agents at scale?
Why ask this? To understand the technical and operational hurdles you may encounter and how the team approaches them.
How does the team balance rapid iteration with maintaining production-quality standards?
Why ask this? To gauge the team's engineering culture and expectations around code quality and speed.
Which frameworks or tools have proven most effective for your agentic AI systems, and why?
Why ask this? To learn about the team's technology choices and rationale, helping you align your experience.
How is success measured for AI agents in production?
Why ask this? To clarify the evaluation metrics and monitoring practices used by the team.
What opportunities exist for contributing to architectural decisions or introducing new technologies?
Why ask this? To assess your potential influence on technical direction and innovation.
How does the team collaborate with other stakeholders (e.g., product, research) during agent development?
Why ask this? To understand cross-functional collaboration and communication expectations.
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