Applied AI Engineer

Position Overview

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 both the analytical depth to collaborate with cross‑functional business teams in identifying and scoping AI opportunities, and the 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.

• 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 AI 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—ensuring 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 that enhance our capabilities.

• 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.

• 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, Lang Chain, and Lang Graph.
• Demonstrated experience building with Generative AI and foundational models (e.g., multimodal, image/video generation) using libraries such as PyTorch, Tensor Flow, Keras.
• Experience applying RAG techniques (including advanced RAG patterns) and agentic AI design patterns in production systems.
• Familiarity with MLOps and Dev Ops practices as applied to AI/ML model lifecycle management, deployment, and monitoring.
• Experience working with SQL and No
  
  SQL databases and performing data manipulation at scale.
• Experience with AI observability tools like Lang Smith or Lang Fuse.
• 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.

• 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 (NLP), computer vision, recommendation systems, and deep learning architectures.
• Experience with AI observability tools and…