AI​/ML Engineer

Overview

The National Endowment for the Arts (NEA) is investing in artificial intelligence and machine learning capabilities to modernize internal operations, improve grant analysis workflows, and enhance public-facing services. The organization requires production-grade AI/ML systems that meet federal data governance, privacy, and accessibility standards while delivering measurable impact across NEA's mission areas.

Responsibilities

• Design, develop, and deploy machine learning models and AI systems tailored to NEA's business needs across both structured and unstructured data sources.
• Conduct data preprocessing, feature engineering, model selection, training, evaluation, and validation across the full ML lifecycle.
• Build and operate production-grade machine learning pipelines using Python, Tensor Flow, PyTorch, scikit-learn, and Orange.
• Develop and integrate Large Language Model (LLM) capabilities, including Retrieval-Augmented Generation (RAG), embedding models, and prompt engineering for domain-specific tasks.
• Design and implement model deployment workflows on AWS and Microsoft Azure, including managed services such as Sage Maker, Bedrock, Azure Machine Learning, and Azure OpenAI.
• Implement MLOps practices including model versioning, experiment tracking, automated retraining, drift detection, and rollback capabilities.
• Develop CI/CD pipelines for machine learning workloads using Git Hub Actions, Azure Dev Ops, Jenkins, or equivalent tools.
• Containerize machine learning services using Docker and orchestrate deployments on Kubernetes (AKS, EKS) for scalability and resilience.
• Build Python-based REST APIs and asynchronous backend services for model inference, batch processing, and real-time prediction using frameworks such as FastAPI and Flask.
• Integrate machine learning components with relational and non-relational databases including Postgre SQL, MySQL, and Mongo DB.
• Monitor model performance in production, implement observability through tools such as Azure Monitor, Prometheus, and Grafana, and retrain or update models as data and business needs evolve.
• Implement responsible AI practices including model explainability (SHAP, LIME), bias detection, fairness audits, and adherence to data governance and privacy standards.
• Conduct architectural peer reviews for code created by other engineers and contribute to engineering standards across the AI/ML platform.
• Set up build, test, staging, and production environments and deploy code through structured release processes.
• Contribute to estimations for all tickets in the backlog and participate in Backlog Grooming, Sprint Planning, and Sprint Review meetings.
• Adhere to Agile SCRUM methodologies and organizational delivery processes.
• Work closely and collaboratively with federal and contractor personnel to develop solutions that align with NEA mission objectives.
• Share knowledge and expertise with colleagues, mentoring and guiding less experienced engineers through code reviews, design reviews, and best-practice guidance.
• Stay current with advancements in AI/ML technologies, including foundation models, agentic AI, fine-tuning techniques, and emerging frameworks, and recommend appropriate solutions for NEA initiatives.

Requirements

Required Education and Experience

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.
• 5+ years of hands-on experience in AI/ML development and deployment in production environments.
• Senior level proficiency in Python, including the ability to develop production-grade backend services, APIs, middleware, and machine learning data pipelines.
• Senior level experience with Tensor Flow, PyTorch, scikit-learn, and Orange for machine learning model development.
• Experience working with both Amazon Web Services (AWS) and Microsoft Azure for hosting, deployment, and scalability of AI/ML workloads.
• Strong understanding of machine learning algorithms, including supervised, unsupervised, and deep learning approaches, along with data structures and software engineering principles.
• Experience…