Senior Applied AI Engineer

About Clariti

Role overview

Key responsibilities

• Own computer vision and agentic AI problem domains, selecting and adapting architectures and training or fine-tuning strategies for complex real-world data.
• Design and execute experiments end to end, evaluate model performance, and determine which approaches to pursue.
• Identify high-impact opportunities to improve AI systems and shape technical direction.
• Balance model accuracy, interpretability, latency, and cost, and communicate decisions to stakeholders.
• Share experience with research teammates and help improve experimental rigor.
• Track techniques, benchmarks, and tools across computer vision, generative AI, NLP, and multimodal learning, and apply useful methods.
• Partner with product and engineering managers to identify where AI can create customer value.

Experience and qualifications

• 7–10 years of hands-on applied AI/ML industry experience and a record of shipping models and systems that solve business problems.
• Experience leading as the sole or lead AI expert and taking work from proof of concept through production without a dedicated ML engineering team.
• Fluency with MLOps practices and infrastructure, including model CI/CD, containerization, deployment, and monitoring.
• Ability to operate independently across cross-functional teams and work with technical and nontechnical stakeholders.
• Strong experience building production agentic AI workflows and LLM applications, including retrieval, tool use, evaluation, and guardrails.
• Deep experience training and fine-tuning models, defining architectures, curating datasets, tuning training parameters, and diagnosing performance.
• Significant computer vision and image-processing experience, including feature extraction, object detection, segmentation, and geometric transformations.
• Familiarity with diffusion models, model distillation, or multimodal architectures in production.
• Strong proficiency in Python.

Areas of work

• The role spans computer vision, agentic AI, LLM applications, generative AI, NLP, and multimodal learning.
• The role applies MLOps practices to deploy and monitor production models.