Role overview
We're looking for a Senior Machine Learning Engineer (Level 2/3) to join our growing ML Engineering team and lead the development of advanced AI systems that power our platform. You'll design and deploy the core intelligence behind our products, with a focus on building autonomous, stateful AI agents capable of reasoning, learning, and acting in dynamic environments.
What you'll work on
- Design and deploy autonomous AI agents, including reasoning loops, memory layers, and orchestration pipelines.
- Build observability and evaluation systems to monitor reasoning, token usage, and model performance, ensuring reliable production behavior.
- Lead the development of multimodal ML pipelines for semantic search, RAG, recommendation systems, and vector search across text, image, and video data.
- Engineer high-throughput time-series analytics and forecasting models that connect batch OLAP queries with real-time inference.
- Develop and maintain scalable asynchronous APIs and containerized services, ensuring reliability, monitoring, and performance optimization.
- Partner with product and engineering teams to translate business goals into measurable ML outcomes.
- Drive research-to-production pipelines for experimental AI projects and evaluate emerging technologies to advance our platform.
What we're looking for
- 4+ years of experience in machine learning engineering, building production-grade ML systems.
- Hands-on experience with agentic AI frameworks (e.g., LangGraph, LlamaIndex, Zep, Mem0, Langfuse, LangSmith).
- Experience building RAG pipelines, recommendation systems, and/or vector search applications (e.g., Pinecone, Vespa, PostgreSQL + pgvector).
- Strong background in time-series modeling, anomaly detection, and large-scale data analysis (e.g., Clickhouse).
- Skilled in asynchronous API design, containerization, and modern CI/CD workflows (FastAPI, Docker, Kubernetes, GitHub/Bitbucket).
- Excellent EDA skills with the ability to translate data insights into production-ready ML solutions.
- Comfortable working with LLM ambiguity, designing systems that fail gracefully and learn continuously.
- Proactive, independent, and curious—able to own complex features end-to-end and raise the technical bar for the team.
- Strong communication skills—able to explain trade-offs between AI approaches and align technical metrics to business goals.
- Experience leveraging AI tools and functionality to improve workflow efficiency, research, and experimentation.
Tags & focus areas
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