Applied AI Engineer

Responsibilities

• Identify, prioritize, and implement high-impact AI and automation opportunities that drive measurable business outcomes, improve decision-making, and support organizational objectives.
• Design, build, test, and deploy scalable, production-ready AI solutions, including intelligent assistants, machine learning applications, and workflow automation tools.
• Streamline business processes through automation and innovative technologies to increase productivity, reduce manual effort, and improve operational effectiveness.
• 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.
• 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.

Qualifications

• Bachelor’s Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, Information Systems, or a related field required.
• 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, performance optimization, reliability, and operational readiness.
• Availability to participate in an on-call support rotation as required.
• Experience working in enterprise, financial services, or other regulated environments preferred.
• Experience with Azure, AWS, Google Cloud Platform, or comparable cloud technologies preferred.
• Experience with machine learning platforms and tools such as Azure Machine Learning, MLflow, Databricks, SageMaker, Vertex AI, or similar technologies preferred.
• Experience building and managing machine learning pipelines including data preparation, training, evaluation, deployment, monitoring, and retraining preferred.
• Experience with AI development platforms and tools such as Microsoft Foundry, Azure OpenAI, Copilot Studio, or similar technologies preferred.
• Practical experience implementing MLOps practices and supporting production AI environments preferred.
• Knowledge of data governance, responsible AI principles, and AI risk management practices preferred.
• Ability to travel periodically to company locations, meetings, training sessions, or business-related events, as needed.