AIML Engineer

Senior AI/ML Engineer

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 NEAs mission areas.

The Senior AI/ML Engineer will design build and deploy machine learning models and AI systems supporting NEAs business needs across structured and unstructured data domains. This role spans the full AI/ML lifecycle including data preprocessing feature engineering model training evaluation deployment and monitoring. The engineer will work within an Agile SCRUM environment collaborating with data scientists software engineers domain experts and federal stakeholders to integrate models into production environments hosted on Amazon Web Services and Microsoft Azure cloud infrastructure.

Design develop and deploy machine learning models and AI systems tailored to NEAs 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 TensorFlow 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 Amazon Web Services (AWS) and Microsoft Azure including managed services such as SageMaker 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 GitHub Actions Azure DevOps 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 database systems including PostgreSQL MySQL and MongoDB;

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

Bachelors or Masters 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 TensorFlow 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 with MLOps practices including CI/CD pipelines model versioning experiment tracking automated retraining and drift detection.

Experience with containerization (Docker) and orchestration (Kubernetes) for AI/ML workloads.

Experience integrating machine learning systems with SQL and NoSQL databases.

Experience with Agile/Scrum methodologies and project management tools (e.g. Azure DevOps Jira).

Demonstrated ability to deliver quality production-grade machine learning systems on time and as estimated within an Agile SCRUM environment.

Active participation in mentoring and guiding less experienced engineers through code reviews and best practices.

Excellent communication collaboration and organizational skills with experience working alongside cross-functional teams and federal stakeholders.

Experience working with Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) agentic AI workflows and fine-tuning of small language models or embedding models.

Familiarity with vector databases and retrieval frameworks (FAISS Pinecone Chroma pgvector) and graph databases (Neo4j Amazon Neptune) for AI/ML retrieval workloads.

Experience with Agile/Scrum development environments in a federal or regulated setting.

Experience working with cross-functional teams across global time zones and cultures.

Working knowledge of accessibility standards (Section 508/WCAG) and federal web requirements where AI/ML systems interact with public-facing applications.

Familiarity with Azure Monitor App Insights Log Analytics Prometheus and Grafana for AI/ML observability and performance monitoring.

Working knowledge of infrastructure automation and configuration management tools (Terraform Ansible).

Understanding of data governance privacy frameworks and ethical AI practices applicable to federal data environments.

Programming Knowledge/competencies in the following technologies:

Python (advanced) including async programming and performance optimization

Machine learning frameworks: TensorFlow PyTorch scikit-learn Keras Orange Hugging Face Transformers

Generative AI: LLM APIs (OpenAI Anthropic Azure OpenAI) prompt engineering RAG fine-tuning

Cloud platforms: AWS (SageMaker Bedrock Lambda S3 EC2 EKS) and Microsoft Azure (Azure ML Azure OpenAI AKS Azure Databricks)

Backend and APIs: FastAPI Flask REST API design async processing

Data pipelines and orchestration: Apache Airflow Kubeflow MLflow

Databases: PostgreSQL MySQL MongoDB vector databases

Containerization and orchestration: Docker Kubernetes

MLOps and CI/CD: GitHub Actions Azure DevOps Jenkins model versioning experiment tracking

Version control with Git

Software testing protocols code review practices and clean coding principles

Benefits