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.

Responsibilities:

• 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.

AI Opportunity Identification and Solution Delivery

• 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.

AI Engineering and Automation Development

• 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.

Machine Learning Operations and Platform Management

• 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.

Governance, Security, and Operational Excellence

• 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.

Vendor Evaluation and Technology Integration

• 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.

Qualifications:

• 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 requirements and determine the most effective approach, including custom development, third-party solutions, or integrated technology platforms.

• Understanding of production support concepts including monitoring…