Associate AI​/ML Data Scientist (Systems Architecture, Associate

Associate AI/ML Data Scientist (Systems Architecture, Associate) Position Location Reston, VA

BA/BS or HS/GED and Equivalent Work Experience

Required

The MIL Corporation is seeking a highly experienced, senior-level Associate AI/ML Data Scientist (Systems Architecture, Associate) to lead the design and implementation of enterprise-scale data platforms to support a federal government customer’s large-scale Agentic AI transformation initiative in Reston, VA.

With extensive experience delivering AI and machine learning solutions in complex enterprise environments, the AI/ML Data Scientist will serve as a technical leader responsible for architecting the data ecosystem that powers vector search, Retrieval-Augmented Generation (RAG), large language model (LLM) applications, and emerging Agentic AI capabilities. This role will work across infrastructure, cybersecurity, software engineering, and mission stakeholders to rapidly deliver scalable, secure, and reliable AI solutions.

This role requires a seasoned engineer capable of communicating complex technical concepts and architectural tradeoffs to senior leaders, helping leadership teams understand the operational, mission, security, and business implications of emerging AI technologies while driving successful implementation of enterprise capabilities.

This position currently requires an on-site schedule. Schedule is subject to change based on company/contract requirements.

Responsibilities

• Enterprise AI Platform Engineering:
  Architect, build, and maintain enterprise-scale data platforms supporting vector databases, semantic search, Retrieval-Augmented Generation (RAG), Agentic AI systems, and large language model applications.
• Data Architecture & Strategy:
  Establish and implement controls for data quality, lineage, source attribution, prompt and context traceability, explainability, and evaluation of AI system outputs.
• AI Data Governance:
  Establish and implement controls for data quality, lineage, source attribution, prompt and context traceability, explainability, and evaluation of AI system outputs.
• Technical Leadership:
  Lead architectural decision-making for AI-supporting data infrastructure, balancing performance, scalability, security, reliability, maintainability, and cost considerations.
• Cross-Functional Integration:
  Partner with Data Scientists, Machine Learning Engineers, Architects, Cybersecurity teams, and Software Engineers to translate AI requirements into production-grade capabilities.
• Executive Communication:
  Translate highly technical AI, machine learning, and data architecture concepts into clear operational impacts, risks, opportunities, and implementation considerations for senior leadership.
• Enterprise Coordination:
  Coordinate with stakeholders across multiple organizations to align AI initiatives, maximize reuse of enterprise capabilities, and eliminate duplication of effort.
• Operational Excellence:
  Implement monitoring, observability, and alerting to ensure reliability, performance, and continuous improvement of AI-supporting data platforms.
• Mentorship & Engineering Excellence:
  Provide technical leadership and mentorship to engineers while promoting engineering best practices and innovation across the organization.
• Technology Evaluation:
  Assess emerging AI technologies, vector database platforms, retrieval frameworks, and engineering approaches to improve organizational AI capabilities.

Travel

None

Required Qualifications

• 8+ years of progressive experience in software engineering, data engineering, distributed systems, cloud architecture, or AI/ML platform development.
• Proven experience designing and delivering enterprise-scale AI, machine learning, generative AI, or Agentic AI solutions.
• Demonstrated success architecting and implementing production cloud-native data systems supporting advanced analytics and AI workloads.
• Proven experience working within complex enterprise environments managing security, infrastructure, technology dependencies, governance requirements, and competing priorities.
• Extensive experience designing data pipelines supporting machine learning models, vector databases, semantic search capabilities, and generative AI…