Senior Applied Ai Engineer

We’re seeking a Senior Applied AI Engineer to serve as a forward-deployed AI expert across Clariti’s product teamsYou’ll turn the latest machine learning research into practical, production-ready features, spanning large language models (LLMs), agentic AI workflows, computer vision, and multimodal learningYou’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 companyOn 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 productsThat 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 infrastructureYou’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 teamThe 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 environmentOwn 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 dataDrive the design and execution of experiments end to end, from framing the question to evaluating model performance and deciding what’s worth pursuingHelp shape technical direction by identifying high-impact opportunities to improve our AI systems, drawing on applied experience and practical judgmentMake pragmatic trade‑off calls across model accuracy, interpretability, latency, and cost, and directly own decisions in communication with stakeholdersShare your experience with research teammates and help raise the bar for experimental rigor in a collaborative environmentStay current on techniques, benchmarks, and tooling across computer vision, generative AI, NLP, and multimodal learning — and bring the useful ones into practiceEmbed with product teams across Clariti, partnering with product and engineering managers to identify where AI can create the most value for our customersBenefits
Flexible Work CultureProfessional DevelopmentTeam Building Events/OutingsMotivated TeamCorporate Bonus PlanDemonstrated 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 teamFamiliarity with diffusion models, model distillation, or multimodal architectures in productionDeep, 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 performanceSharp experimental and statistical intuition;
comfortable making decisions from messy, incomplete results and separating critical unknowns from nice-to-havesStrong proficiency in Python and experience with PyTorch, TensorFlow, JAX, or similar deep learning frameworksHands‑on fluency with MLOps practices and infrastructure, including CI/CD for models, containerization, deployment, and monitoring7–10 years of hands‑on experience in applied AI/ML in industry, with a track record of shipping models and systems that solved real business problemsStrong experience building agentic AI workflows and LLM‑powered applications in production, including retrieval, tool use, evaluation, and guardrailsExcellent communication skills: you can align stakeholders, justify trade‑offs, write clear design proposals, and explain AI concepts to engineers, executives, and customers alikeStrong software engineering skills, including Git, testing, API design, and modular code that integrates cleanly into existing products and that non‑ML engineers can maintainHands‑on experience with end‑to‑end ML workflows on cloud platforms such as AWS, Azure, or GCPA proven ability to operate independently inside cross‑functional teams, working closely with product managers, engineering managers, and nontechnical stakeholdersSignificant experience with computer vision and image processing, including feature extraction, object detection, segmentation, and geometric transformationsInterest in the ethical implications of AI technology and responsible AI development and designExperience in a forward‑deployed, embedded, or solutions engineering role, bringing AI into teams or products that didn’t have it beforePreference for or comfort thriving in an early‑stage or startup environmentExperience developing computer vision systems requiring high accuracy and reliability under strict tolerances — for example, in autonomous vehicles, medical imaging, or industrial inspectionExposure to the construction, building, civic‑tech, or regulatory/compliance domains
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