Applied AI Engineer

Position Description The AI Engineer will serve as a critical bridge between business stakeholders and technical implementation, translating complex organizational challenges into practical, high-impact AI solutions. This hands‑on role requires analytical depth to collaborate with cross‑functional business teams in identifying and scoping AI opportunities, as well as engineering expertise to design, build, and deploy those solutions. Working closely with data scientists, software engineers, and business partners, the AI Engineer will drive end‑to‑end delivery of Generative AI, Machine Learning, and advanced AI capabilities that create measurable business value. Responsibilities Partner with business teams to gather requirements, translate objectives into AI problem statements, and design solutions aligned with strategic goals. Design, build, and deploy AI solutions leveraging Generative AI, Large Language Models (LLMs), Machine Learning, and other advanced techniques to solve real-world business problems. Participate in discovery sessions and brainstorming workshops to identify new AI use cases, evaluate feasibility, and prioritize initiatives by business impact. Train, fine‑tune, validate, and optimize machine learning models for performance, scalability, and accuracy in production environments. Implement Retrieval‑Augmented Generation (RAG) pipelines, AI agentic patterns, and multi‑modal model architectures to address complex use cases. Collaborate with data engineers to collect, preprocess, and clean structured and unstructured data; apply feature engineering, augmentation, and transformation techniques. Deploy AI models to production, establish monitoring and observability frameworks, and implement continuous feedback loops for ongoing improvement. Troubleshoot issues with deployed models—addressing hallucinations, drift, latency, and availability—to ensure reliability and scalability. Document model development processes, architecture decisions, code, and performance metrics; promote reproducibility and modularity. Champion best practices in responsible AI development, including ethical guidelines, explainability, and bias mitigation. Stay current with emerging AI technologies, especially in Generative AI and LLMs, and proactively recommend tools and approaches to enhance capabilities. Required Qualifications Educational Background Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field. Relevant certifications in AI, ML, or cloud platforms are a plus. Experience 3+ years of hands‑on experience developing and deploying machine learning and AI models, preferably in an industrial, manufacturing, or enterprise context. Proven experience with AI development platforms and frameworks including Google’s ADK, Claude API (Anthropic), Microsoft Azure AI Foundry, Copilot Studio, OpenAI APIs, Hugging Face, LangChain, and LangGraph. Demonstrated experience building with Generative AI and foundational models (e.g., multimodal, image/video generation) using libraries such as PyTorch, TensorFlow, and Keras. Experience applying RAG techniques and agentic AI design patterns in production systems. Familiarity with MLOps and DevOps practices as applied to AI/ML model lifecycle management, deployment, and monitoring. Experience working with SQL and NoSQL databases and performing data manipulation at scale. Experience with AI observability tools like LangSmith or LangFuse. Experience deploying AI agents to production, including implementation of safety guardrails, output validation, rate limiting, and escalation controls to ensure reliable and responsible operation at scale. Technical Skills Strong proficiency in Python, SQL, and relevant AI/ML libraries and frameworks. Hands‑on experience with cloud platforms (AWS, Azure, or GCP) and MLOps tooling for model deployment, versioning, and performance monitoring. Solid understanding of machine learning algorithms, natural language processing, computer vision, recommendation systems, and deep learning architectures. Experience with AI observability tools and techniques for performance tracking, drift detection, and hallucination troubleshooting. Familiarity with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate model insights. Understanding of data preprocessing techniques and tools for handling large‑scale datasets. Soft Skills & Competencies Strong analytical and problem‑solving skills with a focus on practical, business‑relevant applications. Excellent communication skills with the ability to translate technical findings into clear insights for non‑technical stakeholders. Collaborative mindset with experience working in cross‑functional teams spanning engineering, data, and business. Comfortable operating in a fast‑paced, agile environment; able to manage multiple concurrent projects and reprioritize based on business needs. #J-18808-Ljbffr