Artificial Intelligence / Machine Learning Subject Matter Expert

Artificial Intelligence / Machine Learning Subject Matter Expert (AI/ML SME)Position Overview

We are seeking an experienced Artificial Intelligence / Machine Learning Subject Matter Expert (AI/ML SME) to lead the design, development, integration, and deployment of advanced AI-enabled solutions that automate customer workflows and improve business processes.

The AI/ML SME will work closely with customers, product owners, data scientists, software engineers, system architects, and mission stakeholders to understand operational requirements and translate them into scalable AI and machine learning solutions. This role requires deep expertise in machine learning frameworks, software architecture, data modeling, algorithms, statistics, and AI model integration.

The successful candidate will provide technical leadership across the full AI/ML lifecycle, including requirements analysis, data preparation, model development, model training, validation, deployment, integration, monitoring, and continuous improvement.

Key Responsibilities

• Engage with customers and mission stakeholders to understand existing workflows, business processes, operational challenges, and automation opportunities.

• Analyze customer requirements and translate them into AI/ML use cases, technical designs, system architectures, and implementation plans.

• Design and implement AI-enabled solutions that automate repetitive, data-intensive, and decision-support workflows.

  
  

• Evaluate business processes to determine where machine learning, predictive analytics, natural language processing, computer vision, or other AI technologies can improve efficiency and accuracy.

• Develop scalable data structures, data models, and software architectures supporting AI and machine learning applications.

• Design end-to-end AI/ML pipelines for data ingestion, preprocessing, feature engineering, model training, validation, deployment, and monitoring.

• Train, test, tune, and validate machine learning and deep learning models using large and complex datasets.

  
  

• Integrate pre-trained AI and machine learning models into existing dataflows, enterprise applications, APIs, and software architectures.

• Evaluate, select, and implement appropriate machine learning algorithms, tools, frameworks, and development environments.

• Develop production-ready AI solutions using frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or comparable technologies.

• Develop AI/ML applications using Python, Java, C++, Conda, and related programming environments.

  
  

• Apply probability, statistics, linear algebra, calculus, optimization, and algorithmic analysis to the development and validation of AI models.

• Develop predictive, classification, clustering, recommendation, anomaly-detection, and optimization models.

• Assess model accuracy, performance, scalability, bias, explainability, and operational suitability.

• Develop reusable AI components, libraries, APIs, and services that can be integrated across multiple enterprise systems.

  
  

• Design software interfaces supporting model inference, batch processing, real-time processing, and model-serving environments.

• Support integration of AI solutions into cloud, on-premises, and hybrid computing environments.

• Establish AI/ML engineering standards, development practices, testing procedures, model-governance controls, and technical documentation.

• Conduct technical reviews of AI architectures, code, models, algorithms, and data pipelines.

  
  

• Troubleshoot model, data, performance, integration, and deployment issues throughout the AI/ML lifecycle.

• Monitor production AI systems and recommend improvements based on model performance, data drift, operational changes, and customer feedback.

• Mentor engineers and data scientists in machine learning engineering, software architecture, algorithm development, and AI implementation.

• Present complex AI concepts, technical findings, risks, and recommendations to both technical and nontechnical stakeholders.

  
  

Required Qualifications

• Extensive professional experience designing, developing, and implementing artificial intelligence and machine learning solutions.

• Demonstrated experience automating customer workflows and business processes using AI technologies.

• Strong experience with data structures, data modeling, software architecture, and enterprise application design.

  
  

• In-depth experience with machine learning and deep learning frameworks, including:

  • 
    

    PyTorch

  • 
    

    TensorFlow

  • 
    

    Keras

  • 
    

    Scikit-learn

  • Comparable AI/ML frameworks

  
  

• Advanced programming experience with one or more of the following:

  • 
    

    Python

  • 
    

    Java

  • 
    

    C++

  • 
    

    Conda

  • 
    

    SQL

  
  

• Demonstrated experience training, testing, tuning, validating, and deploying AI and machine learning models.

• Experience integrating pre-trained models into enterprise dataflows, applications, APIs, and software architectures.

• Strong knowledge of probability, statistics, algorithms, linear algebra, calculus, and mathematical optimization.

• Experience developing supervised, unsupervised, and deep learning solutions.

  
  

• Experience designing and implementing data preprocessing, feature engineering, model-training, and inference pipelines.

• Experience evaluating models using appropriate performance metrics and validation methodologies.

• Strong understanding of model deployment, model serving, monitoring, and lifecycle management.

• Experience designing scalable software systems that support AI and machine learning workloads.

  
  

• Ability to translate business and operational requirements into technical AI solutions.

• Strong analytical, problem-solving, technical-writing, and communication skills.

Preferred Qualifications

• Experience serving as an AI/ML technical lead, architect, or subject matter expert.

  
  

• Experience with natural language processing, computer vision, predictive analytics, recommendation systems, anomaly detection, or generative AI.

• Experience integrating commercial, open-source, or internally developed AI models into enterprise systems.

• Experience with cloud AI/ML platforms such as:

  • 
    

    AWS SageMaker

  • Azure Machine Learning

  • Google Vertex AI

  
  

• Experience with containerization and orchestration technologies such as Docker and Kubernetes.

• Experience with MLOps tools, model registries, automated training pipelines, and continuous integration and continuous deployment.

  
  

• Experience with data engineering technologies, distributed computing, and large-scale data processing.

• Knowledge of responsible AI, model explainability, algorithmic bias, data privacy, and AI governance.

• Experience with RESTful APIs, microservices, event-driven architectures, and enterprise system integration.

• Experience mentoring technical teams and establishing AI/ML development standards and best practices.

  
  

• Experience supporting Department of Defense, Intelligence Community, Federal Government, or other mission-critical environments.

Technical Skills

• Artificial Intelligence

• Machine Learning

  
  
• 
  

  Deep Learning

• 
  

  Neural Networks

• 
  

  PyTorch

• 
  

  TensorFlow

• 
  

  Keras

• 
  

  Scikit-learn

• 
  

  Python

• 
  

  Java

• 
  

  C++

• 
  

  Conda

• 
  

  SQL

• 
  

  Data Modeling

• 
  

  Data Structures

• Software Architecture

• Algorithm Development

• Probability and Statistics

  
  
• 
  

  Linear Algebra

• Mathematical Optimization

• Feature Engineering

• 
  

  Model Training

• Model Validation

• Model Deployment

• Model Integration

• 
  

  MLOps

• 
  

  API Development

• Workflow Automation

• Business Process Automation

• 
  

  Cloud Computing

• 
  

  Docker

• 
  

  Kubernetes

• 
  

  Git

• 
  

  CI/CD

Education

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, Engineering, or a related technical field.

• 
  

  Master’s degree or Ph.D. in a relevant discipline is preferred.

Ideal Candidate Profile

The ideal candidate is a senior technical leader with deep expertise in artificial intelligence, machine learning, mathematics, and software engineering. The candidate can independently assess customer workflows, identify meaningful opportunities for AI automation, and design production-ready solutions that integrate machine learning models into enterprise systems.

This individual should be equally comfortable developing and validating AI models, designing scalable software architectures, mentoring technical teams, and communicating complex AI concepts to customers and executive stakeholders.