Senior Machine Learning Engineer - AI

At Forrester, we're trusted to work on trailblazing, mission critical problems that business and technology leaders face today. We're proud of our community of smart people and vibrant voices who come together to do what's right by our clients and each other. As a Senior Machine Learning Engineer, this individual will be challenged to develop enterprise-grade advanced ML and NLP solutions powering Izola, Forrester's conversational AI chatbot. This individual will architect and optimize its multiagent framework, establish robust answer quality observability and retriever optimization systems, and deliver intelligent, trustworthy, and natural conversations while ensuring scalable, production-ready ML systems that deliver exceptional user experiences.

This role emphasizes senior-level ownership of end-to-end, production ML systems: designing, building, and operating scalable conversational AI capabilities, rather than narrowly scoped model research.

Apply design and coding best practices in writing highly optimized, scalable, and maintainable code for ML systems.

Answer quality: 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.

Collaborate: Work closely with product, design, and engineering teams to deliver multiagent conversational experiences.

Ensure quality: Apply rigorous testing, observability, and performance optimization for Izola's production environment.

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., 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., 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.

Experience with reinforcement learning for dialogue optimization and experimentation (A/B testing, design of experiments).

Experience with voice technologies (speech-to-text and text-to-speech) for conversational AI.

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