Senior Applied AI Engineer

• We’re seeking a Senior Applied AI Engineer to serve as a forward-deployed AI expert across Clariti’s product teams
• You’ll turn the latest machine learning research into practical, production-ready features, spanning large language models (LLMs), agentic AI workflows, computer vision, and multimodal learning
• You’ll report into our CivCheck AI Research team, the home of Clariti’s AI expertise, but your day-to-day work will be embedded with product and engineering managers across the company
• On these projects, you’ll often be the AI specialist, bringing expertise in model selection, deployment, and agentic AI to bring brand-new capabilities in an AI-native transformation of Clariti’s existing products
• That means you’ll drive projects end to end: scoping the problem, building the proof of concept, and taking it all the way to production with our MLOps tooling and infrastructure
• You’ll bring your proposed approaches to the CivCheck AI Research team for design feedback, giving you a strong peer group for technical rigor while you own delivery within your product team
• The ideal candidate has spent years doing applied AI work in industry, is comfortable being the go-to AI person on a cross-functional team, and is energized by hard, open-ended problems in a lean, fast-moving environment
• Own computer vision and agentic AI problem domains, selecting and adapting architectures and training/fine-tuning strategies for the best results on complex, unstructured real-world data
• Drive the design and execution of experiments end to end, from framing the question to evaluating model performance and deciding what’s worth pursuing
• Help shape technical direction by identifying high-impact opportunities to improve our AI systems, drawing on applied experience and practical judgment
• Make pragmatic trade-off calls across model accuracy, interpretability, latency, and cost, and directly own decisions in communication with stakeholders
• Share your experience with research teammates and help raise the bar for experimental rigor in a collaborative environment
• Stay current on techniques, benchmarks, and tooling across computer vision, generative AI, NLP, and multimodal learning — and bring the useful ones into practice
• Embed with product teams across Clariti, partnering with product and engineering managers to identify where AI can create the most value for our customers

Benefits

• Flexible Work Culture
• Professional Development
• Team Building Events/Outings
• Motivated Team
• Corporate Bonus Plan

• Demonstrated experience as the lead or sole AI expert on a project, taking your own work from proof of concept through to production without relying on a dedicated ML engineering team
• Familiarity with diffusion models, model distillation, or multimodal architectures in production
• Deep, demonstrated experience training and fine-tuning models using modern frameworks, including defining architectures, curating and cleaning datasets, tuning training parameters, and diagnosing training and validation performance
• Sharp experimental and statistical intuition; comfortable making decisions from messy, incomplete results and separating critical unknowns from nice-to-haves
• Strong proficiency in Python and experience with PyTorch, TensorFlow, JAX, or similar deep learning frameworks
• Hands-on fluency with MLOps practices and infrastructure, including CI/CD for models, containerization, deployment, and monitoring
• 7–10 years of hands-on experience in applied AI/ML in industry, with a track record of shipping models and systems that solved real business problems
• Strong experience building agentic AI workflows and LLM-powered applications in production, including retrieval, tool use, evaluation, and guardrails
• Excellent communication skills: you can align stakeholders, justify trade-offs, write clear design proposals, and explain AI concepts to engineers, executives, and customers alike
• Strong software engineering skills, including Git, testing, API design, and modular code that integrates cleanly into existing products and that non-ML engineers can maintain
• Hands-on experience with end-to-end ML workflows on cloud platforms such as AWS, Azure, or GCP
• A proven ability to operate independently inside cross-functional teams, working closely with product managers, engineering managers, and nontechnical stakeholders
• Significant experience with computer vision and image processing, including feature extraction, object detection, segmentation, and geometric transformations
• Interest in the ethical implications of AI technology and responsible AI development and design
• Experience in a forward-deployed, embedded, or solutions engineering role, bringing AI into teams or products that didn’t have it before
• Preference for or comfort thriving in an early-stage or startup environment
• Experience developing computer vision systems requiring high accuracy and reliability under strict tolerances — for example, in autonomous vehicles, medical imaging, or industrial inspection
• Exposure to the construction, building, civic-tech, or regulatory/compliance domains