Job description

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED We are seeking an experienced Lead AI / NLP Engineer specializing in agentic AI frameworks and automated knowledge extraction to join our team supporting critical defense and intelligence customer missions. In this role, you will train and deploy advanced Natural Language Processing (NLP) and Named Entity Recognition (NER) models for Large Language Model (LLM) solutions using LangGraph. You will lead both supervised and unsupervised model training and validation workflows to perform dynamic knowledge extraction across multilingual, unstructured data streams without a predefined ontology. Additionally, you will work closely with customer data sources, implement robust data quality techniques, and navigate enterprise compliance and policy requirements to ensure end-user readiness and accessibility. The annual base salary range for this role is $156,000-$184,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things. • Agentic AI & LangGraph: Hands-on proficiency developing, orchestrating, and training NLP/NER capabilities for LLM solutions within agentic AI frameworks (specifically LangGraph). • NLP & Knowledge Extraction: Strong experience in both supervised and unsupervised model training, fine-tuning, and validation for automated knowledge extraction from unstructured, multi-language natural language data without relying on a predefined ontology. • Data Retrieval & Preprocessing: Deep understanding of customer data sources, retrieval techniques, and data preprocessing pipelines required to generate high-quality training and evaluation datasets. • Data Quality & Pipeline Readiness: Mastery of techniques to validate, clean, and enrich training data to ensure high data quality and seamless system integration. • Enterprise Compliance & Governance: Strong understanding of enterprise data compliance, security policies, and governance protocols to ensure AI solutions meet regulatory standards and end-user access controls. • • Education: Bachelor’s Degree in Data Science, Computer Science, Computational Linguistics, Mathematics, or a related technical discipline, PLUS 10 + years of professional experience in data science, NLP, or software engineering OR Associate's Degree PLUS 12+ years of specialized technical experience in lieu of a degree. Multilingual NLP: Experience with cross-lingual embeddings, multilingual transformer architectures, and low-resource language processing. Vector Databases & RAG: Familiarity with Retrieval-Augmented Generation (RAG) architectures, vector stores (e.g., Qdrant, ChromaDB, PGVector, Pinecone), and semantic search indexing. Modern AI Tech Stack: Proficiency in Python and modern ML/DL libraries (PyTorch, Hugging Face Transformers, SpaCy, LangChain). MLOps & Deployment: Experience deploying containerized AI microservices (Docker, Kubernetes) and setting up MLOps pipelines for continuous model monitoring and evaluation.

Requirements

Must-haves

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
Agentic AI & LangGraph: Hands-on proficiency developing, orchestrating, and training NLP/NER capabilities for LLM solutions within agentic AI frameworks (specifically LangGraph).
Data Retrieval & Preprocessing: Deep understanding of customer data sources, retrieval techniques, and data preprocessing pipelines required to generate high-quality training and evaluation datasets.
Data Quality & Pipeline Readiness: Mastery of techniques to validate, clean, and enrich training data to ensure high data quality and seamless system integration.
Enterprise Compliance & Governance: Strong understanding of enterprise data compliance, security policies, and governance protocols to ensure AI solutions meet regulatory standards and end-user access controls.
Multilingual NLP: Experience with cross-lingual embeddings, multilingual transformer architectures, and low-resource language processing.
Vector Databases & RAG: Familiarity with Retrieval-Augmented Generation (RAG) architectures, vector stores (e.g., Qdrant, ChromaDB, PGVector, Pinecone), and semantic search indexing.
Modern AI Tech Stack: Proficiency in Python and modern ML/DL libraries (PyTorch, Hugging Face Transformers, SpaCy, LangChain).
MLOps & Deployment: Experience deploying containerized AI microservices (Docker, Kubernetes) and setting up MLOps pipelines for continuous model monitoring and evaluation.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aillmaccessibilitypythonmldockerkubernetes

Platform: agile-ats
Effort: medium