Description

You will build and operationalize AI/ML pipelines to support DoD technical missions.

Responsibilities

• Develop and operationalize NLP solutions for large datasets using techniques like context, topic, and keyword extraction.
• Build, train, and deploy GPU-based models optimized for performance across distributed compute environments.
• Apply MLOps practices using MLflow for model lifecycle management and tracking.
• Integrate AI capabilities with Elasticsearch and Neo4j to enhance search and graph analytics.
• Manage the full lifecycle of AI/ML components, from research through monitoring and iteration.

Required Skills

• 5+ years of hands-on experience with Natural Language Processing (NLP), Large Language Models (LLMs), semantic search, text embedding, RAG, and generative AI.
• 4+ years experience as an ML Engineer in Databricks, building and managing distributed ML pipelines.
• Strong Python expertise, including developing Flask APIs and reusable ML utilities.
• Proficiency with MLOps and MLflow for model tracking and deployment automation.
• Demonstrated experience with Apache Spark or Databricks for distributed data and ML workloads.
• Hands-on experience developing and tuning GPU-based models in production.
• Experience with SQL and working with petabyte-scale datasets.
• Proficiency with version control systems like Git.
• Bachelor's degree with 5+ years of relevant experience.

Key Skills

Education

Bachelor's degree