Machine Learning Engineer - SME

Key Responsibilities

• Design, develop, fine-tune, and optimize machine learning and deep learning models, including Large Language Models (LLMs), for mission-focused use cases.
• Develop AI/ML pipelines, perform data preprocessing, feature engineering, model training, and performance evaluation.
• Conduct benchmarking, testing, validation, and experimentation to assess the effectiveness and reliability of AI systems, with emphasis on LLM-based solutions.
• Build and evaluate model ensembles and identify optimal algorithms, frameworks, and Python libraries for specific applications.
• Leverage GPU-accelerated computing environments to improve model training and inference performance.
• Develop applications supporting semantic search, information retrieval, and natural language processing capabilities.
• Collaborate with cross-functional teams to translate operational requirements into AI-enabled solutions.
• Create visualizations, dashboards, and reporting solutions to communicate model performance and insights.
• Maintain code repositories and software development best practices using modern version control systems.

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or a related technical field (Master's or Ph.D. preferred).
• Demonstrated professional or academic experience developing and tuning machine learning, deep learning, and LLM-based solutions.
• Proficiency in Python and/or R for machine learning and data analytics.
• Experience with deep learning frameworks such as:
  • PyTorch
  • TensorFlow
  • Keras
• Strong SQL expertise, including:
  • Common Table Expressions (CTEs)
  • Set operations
  • Aggregate functions
  • Nested subqueries
• Experience with version control tools such as GitHub and Jenkins.
• Experience leveraging GPUs and high-performance computing environments for AI/ML workloads.
• Hands-on experience testing, validating, and evaluating AI systems, particularly LLM-based applications.
• Experience designing and executing experiments to benchmark model performance.

Preferred Qualifications

• Experience with the Hugging Face Transformers ecosystem and model hub.
• Experience developing benchmarks and evaluation frameworks for LLM performance measurement.
• Knowledge of cloud-based AI/ML architectures and development environments.
• Experience with web application development frameworks such as Flask.
• Experience building machine learning solutions for:
  • Text classification
  • Topic modeling
  • Natural language processing (NLP)
• Experience developing semantic search applications and retrieval solutions.
• Experience creating data visualizations and dashboards using Tableau.
• Familiarity with Scikit-Learn and traditional machine learning techniques alongside deep learning methodologies.

Desired Technical Skills

• Large Language Models (LLMs)
• Generative AI
• Natural Language Processing (NLP)
• Machine Learning Operations (MLOps)
• Semantic Search
• Deep Learning
• GPU Computing
• Python
• SQL
• PyTorch
• TensorFlow
• Hugging Face
• Flask
• Tableau
• GitHub
• Jenkins

Clearances / Additional Requirements

• Ability to obtain and maintain any required government security clearance.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently as a technical lead and trusted advisor in AI/ML initiatives.