Senior ML Engineer developing and scaling multitemporal, multimodal models for Earth observation using satellite imagery at LiveEO. The role involves applied research and engineering with real - world impacts.
About the role
- Senior Machine Learning Engineer developing advanced ML and NLP solutions for Forrester’s conversational AI chatbot. Collaborating with cross-functional teams to deliver scalable, production-ready ML systems.
Responsibilities
- Apply design and coding best practices in writing highly optimized, scalable, and maintainable code for ML systems.
- Design and optimize Izola’s multiagent orchestration for dynamic, context-aware dialogue across modalities.
- Build and maintain answer quality monitoring pipelines using platforms like Arize and LangSmith, and fine-tune in our retrieval-augmented generation (RAG) architecture to improve relevance and accuracy.
- Research and apply cutting-edge generative AI, NLP, and reinforcement learning techniques to enhance Izola’s conversational intelligence as requirements evolve.
- Work closely with product, design, and engineering teams to deliver multiagent conversational experiences.
- Apply rigorous testing, observability, and performance optimization for Izola’s production environment.
Requirements
- Five-plus years designing and building enterprise-grade, cloud-native applications on public clouds (AWS preferred).
- Four-plus years in ML engineering, owning end-to-end data pipelines for scalable AI systems (e.g., search, recommendations).
- Deep expertise in LLM-powered conversational systems and RAG architectures, including multisource indexing and ingestion for semantic and keyword hybrid retrieval.
- Proven experience in designing and operating multiagent systems using modern orchestration frameworks (e.g., Agno, Google ADK).
- Experience in integrating ML observability platforms (e.g., Arize, LangSmith) to support evaluation and production readiness.
- Hands-on expertise in retriever tuning and personalization, balancing relevance, latency, and scale.
- Advanced proficiency in Python and cloud-native development, with production ownership in AWS environments.
- Strong experience with MLOps practices and scalable ML architectures, from experimentation to production deployment.
- Demonstrated ability to solve ambiguous, high-impact problems and influence outcomes across cross-functional teams.
- Excellent communication skills, with a track record of translating complex technical concepts into clear guidance for diverse stakeholders.
Benefits
- Competitive salary
- Professional development opportunities
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