Senior Agentic AI​/ML Engineer

Senior Agentic AI/ML Engineer

Iron EagleX is seeking a Senior Agentic AI/ML Engineer to support our AI team in Crystal City, VA. This role will design, develop, deploy, and sustain machine learning, artificial intelligence, and agentic AI capabilities that support advanced analytics, automation, and mission-focused decision-making. The AI/ML Engineer will work across the full AI development lifecycle, including data preparation, model development, agentic workflow design, evaluation, integration, deployment, and operational sustainment.

As a Senior Agentic AI/ML Engineer, you will be a core member of the AI team responsible for building practical, reliable, and scalable AI, machine learning, and agentic AI solutions. You will help transform complex data, emerging AI concepts, and mission needs into production-ready capabilities that improve workflows, enhance analytics, automate complex tasks, and support mission outcomes.

Job duties (include but are not limited to):

• Design, develop, train, evaluate, and deploy machine learning models to support mission and business use cases.
• Develop clean, maintainable, and efficient Python code while adhering to best practices and coding standards.
• Build and maintain AI/ML pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
• Design, develop, and integrate modern AI model capabilities into applications and workflows, including prompting, tool calling, RAG, structured outputs, and agentic patterns.
• Develop and support agentic AI workflows that can reason over tasks, use tools, retrieve relevant information, execute multi-step processes, and interact with external systems under defined controls and guardrails.
• Evaluate agentic systems for accuracy, reliability, safety, task completion, tool-use effectiveness, latency, and operational suitability.
• Support the development of AI-enabled applications, analytics tools, automation capabilities, and decision-support systems.
• Collaborate with data engineers, software engineers, analysts, and stakeholders to define requirements and deliver effective AI/ML and agentic AI solutions.
• Design, develop, and integrate APIs, services, and external tools to enable AI/ML and agentic capabilities within larger software systems.
• Establish and maintain reproducible environments, CI/CD workflows, and container-based deployments using tools such as Docker.
• Work with structured and unstructured data sources to ensure data quality, integrity, usability, and performance.
• Implement and maintain version control using Git to streamline collaboration and code management.
• Ensure all developed solutions meet high standards for security, quality, reliability, explainability, and maintainability.
• Contribute to all stages of the AI/ML, agentic AI, and software development life cycles, from concept and experimentation through testing, deployment, monitoring, and sustainment.

Required skills:

• Proficiency in Python and commonly used AI/ML libraries and frameworks.
• Hands-on experience developing, training, evaluating, and deploying machine learning models.
• Strong understanding of supervised learning, unsupervised learning, model evaluation, feature engineering, and data preprocessing techniques.
• Experience working with structured and/or unstructured data in support of analytics or AI/ML use cases.
• Familiarity with modern AI and language model concepts, including prompting, embeddings, retrieval-augmented generation, structured outputs, tool calling, and model evaluation.
• Experience designing, developing, or integrating agentic AI workflows or applications using LLMs, tools, APIs, memory, retrieval, and multi-step task execution.
• Understanding of agentic AI concepts, including task planning, goal decomposition, tool selection, orchestration, human-in-the-loop review, guardrails, and evaluation of agent behavior.
• Experience designing or supporting AI/ML pipelines and reproducible development environments.
• Understanding of MLOps concepts, including model versioning, experiment tracking, model registry, CI/CD, monitoring, and deployment of ML-enabled systems.
• Familiarity with LLMOps or agent operations concepts,…