Senior ML Engineer

Job Description -

The end client is the Department of Defense

Candidates must have security clearance and work in a hybrid environment, 3 days per week on-site in Herndon, VA This is an urgent role for them and would like someone to start in the next 1-2 weeks.

Senior ML Engineer Clearance: Secret Local to Herndon, VA Candidates Need candidates LinkedIn
What You ll Do:
Seeking an advanced Senior ML Engineer with NLP to design, build, and deploy scalable AI/ML models for use within the DoD's Search Portfolio. This role requires a strong background in natural language processing, generative AI (LLMs, RAG), distributed computing, and cloud-native architecture. The successful candidate will collaborate with interdisciplinary teams and apply the latest advancements in AI research to deliver secure, mission-ready solutions that process and analyze massive datasets.
Responsibilities will include but are not limited to:

• Design, develop, test, and support AI/ML pipelines on Databricks using Python to support a variety of Department of Defense (DoD) technical missions.
• Develop and operationalize NLP solutions for large datasets using modern techniques such as context extraction, topic extraction, and keyword extraction (e.g., RAKE, TF-IDF, and other statistical or embedding-based methods).
• Leverage advanced NLP libraries and frameworks including Spark NLP, Hugging Face, and TensorFlow to design and deploy scalable machine-learning models.
• Build, train, and deploy GPU-based models optimized for performance and cost-efficiency across distributed compute environments (Apache Spark/Databricks/Kubernetes).
• Apply MLOps best practices using MLflow for model lifecycle management, experiment tracking, and reproducibility.
• Integrate AI capabilities with Elasticsearch and Neo4j to enhance search, graph analytics, and semantic understanding across enterprise datasets.
• Collaborate with cross-functional teams of data scientists, software engineers, and mission stakeholders to integrate AI/ML capabilities across the Search Portfolio and other data products.
• Manage the full lifecycle of AI/ML components, from research and model development through deployment, monitoring, and iterative improvement.
• Diagnose and solve complex data challenges using analytical modeling, AI-driven reasoning, and modern informatics techniques.
• Document and present technical design alternatives, trade-offs, and implementation strategies to technical and non-technical stakeholders.
• Build and maintain shared ML tools, libraries, and reusable assets to accelerate innovation and ensure engineering consistency.
• Support strategic AI roadmap development and architectural planning to enable rapid prototyping and experimentation with advanced AI capabilities.
• Ensure compliance, security, and traceability in all AI/ML workflows and infrastructure aligned with DoD and federal standards.

What You ll Need:

• Bachelor s degree with 5 years of relevant experience
• 5+ years of hands-on experience with Natural Language Processing (NLP), Large Language Models (LLMs), semantic search, text embedding, Retrieval-Augmented Generation (RAG), and generative AI applications.
• Extensive knowledge of NLP techniques for large datasets, including context, topic, and keyword extraction methods.
• Proficiency in NLP libraries and frameworks such as Spark NLP, Hugging Face, and TensorFlow.
• 4+ years of experience working in Databricks as an ML Engineer, including building and managing distributed ML pipelines.
• Strong Python expertise, including experience developing Flask APIs and reusable ML utilities.
• Experience with MLOps and MLflow for model tracking, deployment automation, and governance.
• Hands-on experience developing and tuning GPU-based models in production environments.
• Working knowledge of Elasticsearch and Neo4j preferred for search and graph-based AI applications.
• Deep understanding of machine-learning subfields such as computer vision, reinforcement learning, and statistical learning theory.
• Proven experience with data preprocessing, feature engineering, and model evaluation.
• Proficiency with version control systems (e.g., Git) for collaborative ML development.
• Demonstrated experience with Apache Spark or Databricks for distributed data and ML workloads.
• Experience working with petabyte-scale datasets, data exploration, SQL, and visualization tools.