About the job
Join Profound in our quest to empower businesses to effectively manage their AI landscape. As an AI and ML Engineer, you will be instrumental in the design, development, and deployment of advanced Natural Language Processing (NLP) and Large Language Model (LLM) systems. Your work will enhance classification, ranking, clustering, topic discovery, and content generation capabilities. You will oversee complete workflows from data acquisition to deployment, collaborating with cross-functional teams to transform genuine user interactions into impactful product features and content that enhances visibility, engagement, and conversion rates.
Responsibilities
Develop and implement scalable NLP models for classification, ranking, clustering, topic extraction, and summarization.
Design end-to-end LLM workflows for generating contextually relevant content, covering topic discovery, brief creation, outlines, drafts, revision cycles, and publish-ready assets.
Create prompt and template libraries that align with brand voice for various formats, including blogs, landing pages, help documentation, and advertisements, ensuring evidence-based generation and citation retrieval.
Establish evaluation frameworks for generated content, focusing on factual accuracy, coverage, tone, safety, and originality, using rubric-based evaluations and human-in-the-loop reviews.
Monitor content performance across SEO and AEO metrics, engaging in experiments to enhance quality, reduce costs, and minimize latency.
Transform extensive text datasets into production-ready features and signals that provide valuable product insights.
Collaborate with engineering teams to instrument events, maintain data pipelines, and ensure high data quality and observability.
Work closely with product, data, and go-to-market teams to define success metrics and conduct experiments that positively influence customer-facing KPIs.
Qualifications
Demonstrated experience in deploying machine learning systems at scale, particularly with large textual datasets.
Practical experience in developing LLM content systems, including prompting, templating, retrieval-augmented generation, guardrails, and evaluation mechanisms.
Proficiency in SQL and strong skills in Python alongside modern machine learning tools.
In-depth understanding of machine learning quality metrics, capable of designing both offline and online evaluations and monitoring systems.
Creative problem solver, capable of innovating when standard solutions do not meet requirements.
Experience in cross-functional, high-performance teams, effectively contributing to diverse projects.