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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** AllSTEM Connections - **Location:** Ontario, CA, United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computing Platforms, Microsoft Azure, Code Generation, Code Review, Programming Tools, Monitoring of Systems, Python (Programming Language), Machine Learning, Scrum Methodology, Software Architecture, Systems Development Life Cycle, Tensorflow, Software Deployment, Software Engineering, Systems Integration, Google Cloud, Pytorch, Large Language Models, Prompt Engineering, Generative AI, Scikit Learn, Information Technology, Process Control Systems, Machine Learning Operations, Data Pipelines - **Published:** September 3, 2026 - **Apply:** https://www.dice.com/job-detail/99ea1c45-6561-43ee-acaa-fd44bd61f1e9 ## About the Role Education: Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field. Experience Baseline: oMinimum of 7+ years of professional software engineering experience with a strong foundation in modern development practices. oMinimum of 5+ years of hands-on experience developing, deploying, and maintaining machine learning solutions. Technical Mastery: oFluent in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). oDemonstrated experience building and integrating Generative AI and Large Language Model (LLM) solutions. oSolid familiarity with major cloud platforms (AWS, Azure, or Google Cloud Platform). oDeep understanding of software architecture, secure APIs, data pipelines, and production deployment practices. Core Competencies: Excellent communication, cross-functional collaboration, and technical leadership skills; strong analytical problem-solving abilities. Preferred Attributes Hands-on experience with Retrieval-Augmented Generation (RAG), vector databases, and advanced prompt engineering. Experience deploying and optimizing AI solutions in edge, embedded, or IoT environments. Knowledge of MLOps practices, model monitoring, drift detection, and AI governance. Familiarity with complex industrial controls, automation systems, smart infrastructure, or enterprise IoT domains. Experience supporting Agile/Scrum development teams in fast-paced product environments. ## Description We are seeking an innovative, technically accomplished Senior AI/ML Engineer to lead the transformation of our next-generation software platforms into AI-native experiences. In this dual-focus role, you will be responsible for building cutting-edge AI-powered product capabilities-such as intelligent copilots, automated analysis, and natural language interfaces-while simultaneously elevating AI engineering practices and developer velocity across the broader engineering organization. This is a high-impact leadership and execution role for an engineer who thrives at the intersection of applied machine learning, software architecture, and developer tooling. You will partner closely with product leaders, software engineers, and data scientists to design scalable inference architectures, deploy robust LLM solutions, and champion responsible AI adoption throughout the software development lifecycle. If you possess a deep background in modern AI/ML development and a passion for engineering excellence, we want to hear from you., Pillar 1: Build AI-Powered Products Model Design & Deployment: Design, build, and deploy production-grade machine learning models and GenAI capabilities across cloud, edge, and on-premises environments. LLM Features: Develop advanced LLM-powered product features, including intelligent user copilots, automated diagnostic analysis, smart recommendations, and natural language interfaces. Inference Architecture: Build scalable, high-performance inference and deployment architectures to support heavy production AI workloads. Performance & Reliability: Monitor, evaluate, and continuously optimize model performance, accuracy, latency, and reliability in live environments. Pillar 2: Accelerate Team Capability & Velocity AI-Assisted Tooling: Identify, evaluate, and implement AI-assisted developer tools that measurably improve software delivery speed and engineering productivity. SDLC Enhancement: Leverage AI to streamline and enhance code generation, automated software testing, code reviews, and CI/CD pipelines. Standards & Governance: Establish rigorous AI engineering best practices, design standards, and governance frameworks across engineering teams. Mentorship & Evaluation: Mentor engineers on emerging AI technologies, frameworks, and implementation strategies while driving responsible, practical AI usage. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Are Code Reviews Worth It? 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Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)