Senior Data Scientist - AI Systems
New
S
Second Front SystemsNational Security, Defense Tech
DC/Maryland/Virginia, Raleigh/Durham/Chapel Hill, NC, Denver/Colorado Springs, CO, Dallas/Fort Worth, TXFull-TimeSenior
Salary137000 - 180000 USD per year
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Job Details
- Experience
- 5 years, or greater, experience in applied machine learning, data science, or AI engineering, with at least 2 years working with large language models in production.
- Required Skills
- PythonLangChain
Requirements
- 5+ years experience in applied machine learning, data science, or AI engineering
- 2+ years working with large language models in production
- Hands-on experience building agentic AI systems with tool-calling agents, multi-step reasoning pipelines, or DAG-based orchestration (LangGraph, LangChain, or equivalent)
- Strong proficiency in Python and the modern ML/AI ecosystem
- Experience with Retrieval-Augmented Generation, including vector databases, embedding models, chunking strategies, and retrieval evaluation
- Experience with foundation model APIs across multiple providers: AWS Bedrock, OpenAI, Anthropic, Cohere, and Meta
- Proven ability to design and implement quantitative evaluation frameworks for LLM or agentic system outputs
- Demonstrated ability to communicate AI concepts and recommendations to non-technical stakeholders and cross-functional teams
- Strong command of statistical reasoning, probabilistic modeling, and experiment design
Responsibilities
- Design, develop, and improve agentic AI systems using LangGraph and LangChain, focusing on reliability, traceability, and measurable output quality.
- Advance existing capabilities including DAG driven AI and chatbots by improving reasoning, tool-calling accuracy, and inference confidence.
- Develop and mature evaluation frameworks to assess AI-generated outputs across dimensions.
- Improve and extend multi-LLM ensemble systems including consensus scoring methods, model weighting, and aggregation strategies.
- Design and implement fine-tuning and prompt optimization pipelines for domain-specific cybersecurity and compliance use cases.
- Develop AI/ML components for RAG systems, including embedding strategies, retrieval optimization, chunking, and re-ranking.
- Partner with Product and Engineering teams to identify high-leverage opportunities to introduce AI across workflows.
- Develop and document reusable AI-native components, integration patterns, and deployment blueprints.
- Define and promote standards for responsible AI integration: evaluation methodology, explainability, audit logging, data privacy, and model governance.
- Act as an internal AI advisor helping Product teams navigate model selection, agent design, RAG implementation, and output quality.
- Contribute to building the organizational muscle for AI adoption: documentation, knowledge transfer, and structured learning.
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