What are the responsibilities and job description for the AI Engineer position at Conner Strong & Buckelew?
Department: Information Technology
Job Summary
We are seeking a highly skilled AI/ML Engineer with a strong focus on deploying, operationalizing, and scaling machine learning (ML) and artificial intelligence (AI) models. The ideal candidate will be an expert in Azure, leveraging tools like Azure Machine Learning, Azure DevOps, and Kubernetes for end-to-end AI/ML pipelines. This role will focus on ensuring robust model deployment, real-time scalability, and seamless integration with enterprise systems.
Key Responsibilities Model Deployment and Operationalization
Advanced degree (Master’s or Ph.D.) in Computer Science, Data Science, or a related field. Equivalent experience will be considered.
Experience
Minimum of 3 years of on-the-job experience in AI/ML engineering, with a focus on model deployment and MLOps.
Technical Skills
Office environment with moderate noise level; able to work flexible hours if needed.
Conner Strong & Buckelew is proud to be an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion, gender, affectional or sexual orientation, gender identity or expression, national origin, ancestry, nationality, age, disability (physical or mental), marital or domestic partnership or Civil Union status, pregnancy, family medical history or genetic information, atypical cellular or blood trait, military service or any other status protected by law.
Job Summary
We are seeking a highly skilled AI/ML Engineer with a strong focus on deploying, operationalizing, and scaling machine learning (ML) and artificial intelligence (AI) models. The ideal candidate will be an expert in Azure, leveraging tools like Azure Machine Learning, Azure DevOps, and Kubernetes for end-to-end AI/ML pipelines. This role will focus on ensuring robust model deployment, real-time scalability, and seamless integration with enterprise systems.
Key Responsibilities Model Deployment and Operationalization
- Develop, deploy, and manage ML/AI models in production environments using Azure ML and Kubernetes.
- Implement REST APIs for seamless integration of models into applications.
- Manage model versioning, testing, and lifecycle using MLOps practices and CI/CD pipelines.
- Use Docker and Kubernetes for containerized and scalable deployments.
- Optimize model performance for both cloud-based and edge deployments.
- Automate resource scaling to handle high-load scenarios in real-time.
- Monitor model performance with Azure Monitor, MLflow, or similar tools.
- Implement alerting systems to detect and mitigate model drift and degradation.
- Ensure models adhere to privacy, compliance, and ethical guidelines (e.g., NYDFS, CCPA).
- Leverage Azure Machine Learning for training and deploying ML models.
- Use Azure Data Factory and Synapse Analytics for data integration and preparation.
- Integrate AI solutions with Azure Cognitive Services for additional functionality.
- Sound knowledge of security and of integration of Azure AI to MS 365 and end users.
- Research and implement state-of-the-art techniques, including transformer models, Generative Adversarial Networks (GANs), and explainable AI.
- Explore applications of AutoML for rapid model development and deployment.
- Implement and fine-tune Large Language Models (LLMs), including closed-source models like GPT and open-source alternatives.
- Incorporate Natural Language Processing (NLP) techniques using transformer models like BERT and packages like NLTK, SpaCy, and Hugging Face libraries.
- Document deployment pipelines, AI solutions, and operational processes.
- Maintain a centralized code repository and ensure adherence to change management practices.
- Develop and enforce coding and documentation standards for AI/ML workflows.
Advanced degree (Master’s or Ph.D.) in Computer Science, Data Science, or a related field. Equivalent experience will be considered.
Experience
Minimum of 3 years of on-the-job experience in AI/ML engineering, with a focus on model deployment and MLOps.
Technical Skills
- Expertise in Azure technologies (Azure ML, Azure Kubernetes Service, Azure Data Factory), Azure Storage (Vector database, Data Lake, Cosmo DB, Blob).
- Proficiency in containerization and orchestration tools like Docker and Kubernetes.
- Advanced programming skills in Python and frameworks like TensorFlow, PyTorch, and scikit-learn.
- Experience with transformer-based NLP models (e.g., BERT, GPT) and Python libraries like NLTK, SpaCy, and Hugging Face.
- Strong understanding of CI/CD pipelines, DevOps practices, and infrastructure automation.
- Experience with both closed-source Large Language Models (LLMs) like GPT and open-source alternatives.
- Familiarity with monitoring tools and performance metrics for deployed models.
- Knowledge of ethical AI practices and frameworks for bias mitigation and explainability.
- Familiarity with deploying web applications using common programming languages.
- Exceptional problem-solving and analytical skills.
- Effective communication and collaboration skills for working across technical and non-technical teams.
- Self-motivated, detail-oriented, and committed to continuous improvement.
- Ability to research and discuss complex technical topics when working with other teams.
Office environment with moderate noise level; able to work flexible hours if needed.
Conner Strong & Buckelew is proud to be an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion, gender, affectional or sexual orientation, gender identity or expression, national origin, ancestry, nationality, age, disability (physical or mental), marital or domestic partnership or Civil Union status, pregnancy, family medical history or genetic information, atypical cellular or blood trait, military service or any other status protected by law.
