Applied AI Engineer

Overview The Applied AI Engineer is responsible for identifying, developing, and implementing practical artificial intelligence, machine learning, and automation solutions that drive business value, improve operational efficiency, and enhance decision-making. This role partners across the organization to evaluate business opportunities, design and deploy production-ready solutions, integrate third-party AI technologies, and ensure AI systems are secure, scalable, compliant, and operationally sustainable. The position combines technical expertise, business acumen, and innovation to deliver measurable outcomes through AI-enabled transformation.

• AI Strategy and Business Value Delivery – Identify, prioritize, and implement high-impact AI and automation opportunities that drive measurable business outcomes, improve decision-making, and support organizational objectives.
• AI Solution Development and Deployment – Design, build, test, and deploy scalable, production-ready AI solutions, including intelligent assistants, machine learning applications, and workflow automation tools.
• Operational Efficiency and Process Optimization – Streamline business processes through automation and innovative technologies to increase productivity, reduce manual effort, and improve operational effectiveness.
• AI Governance, Reliability, and Risk Management – Ensure AI solutions meet security, compliance, governance, monitoring, reliability, and operational readiness standards while supporting responsible AI practices.

• Identify, assess, prioritize, and deliver high-value AI and automation initiatives aligned with business objectives.
• Partner with stakeholders to evaluate opportunities and determine appropriate AI-driven solutions.
• Develop rapid prototypes and iterate solutions based on business feedback and operational needs.

• Design, develop, implement, and support AI-powered applications, intelligent assistants, and workflow automation solutions.
• Build end-to-end solutions that improve business performance and operational efficiency.
• Apply machine learning technologies, pre-trained models, and cloud-based AI services where appropriate.

• Establish and maintain MLOps practices to support model lifecycle management.
• Oversee model versioning, experiment tracking, testing, deployment, monitoring, drift detection, and retraining processes.
• Support reliable and scalable production AI environments.

• Ensure all AI solutions comply with governance, security, regulatory, reliability, and operational readiness requirements.
• Implement monitoring and support processes that promote system stability and long-term performance.
• Apply responsible AI and data governance principles throughout solution development and deployment.

• Evaluate, select, and implement third-party AI, machine learning, and software-as-a-service solutions.
• Determine the most effective approach for solving business problems, balancing build, buy, and integration decisions.
• Integrate vendor technologies into existing business processes, applications, and infrastructure.

• Bachelor’s Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, Information Systems, or a related field required.
• Equivalent combination of education, professional certifications, and directly related experience may be considered.
• Minimum of five (5) years of experience in software engineering, data science, applied artificial intelligence, machine learning, or a related technical discipline required.
• Demonstrated proficiency in Python or a comparable programming language with strong experience integrating APIs and developing end-to-end technology solutions.
• Experience developing, implementing, or supporting large language models (LLMs), workflow automation, and cloud-based AI/ML services.
• Working knowledge of machine learning concepts, methodologies, and model deployment practices.
• Ability to evaluate business…