BOOST LLC is a dynamic management consulting firm that offers an array of government-compliant back-office solutions to support our teaming partners within the GovCon space. Our consultants are experts in the areas of Accounting, Contracts, Human Resources, Recruiting & Sourcing, and Strategic Pricing and our passion is to guide and propel our partners towards success within this competitive sector.
BOOST is partnering with a cleared small business that provides mission-critical, AI-driven cyber defense and reverse engineering solutions for U.S. Government clients to hire an AI/ML SME. This opportunity is 100% onsite located in the Northern Virginia area.

POSITION SUMMARY:

Serve as an AI/ML SME practitioner designing, tuning, and validating machine-learning pipelines that power anomaly detection, retrieval-augmented generation (RAG), and agentic automation at enterprise scale. You will shape technical direction, mentor junior data scientists, and embed robust MLOps practices that deliver decisive advantages to national-security missions.

REQUIRED QUALIFICATIONS:

• Eight plus (8+) years of hands-on machine-learning engineering or data-science experience, at least 3 in DoD/IC or other highly regulated environments.
• Deep proficiency with LLMs and vector databases.
• Demonstrated track record delivering ML solutions for anomaly detection, NLP, or cyber-focused analytics.
• Strong Python software-engineering skills (testing, packaging, CI/CD).
• Commitment to mission-first, people-always leadership with zero tolerance for mission failure.

PREFERRED QUALIFICATIONS:

• Bachelor’s or Master’s in Computer Science, Data Science, Electrical Engineering, or a related technical field.
• Publications or open-source contributions in AI or database security, or adversarial ML.
• Experience with LangChain, Agno, TinyTroupe or similar agentic frameworks.
• Familiarity with accredited TS/SCI cloud services.

• Build, fine-tune, and leverage models for classification, clustering, time-series anomaly detection, and natural-language processing.
• Design and implement end-to-end MLOps workflows, including data ingestion, feature engineering, model training, model registry, CI/CD deployment, and drift monitoring, leveraging state-of-the-art tools such as Apache Airflow, Feast, MLflow, Kubeflow, Terraform, and Prometheus/Grafana.
• Implement retrieval pipelines with vector databases and large-language-model agentic orchestration
• Perform cost/benefit analyses of emerging AI techniques (multi-agent LLMs, advanced embeddings) and present recommendations to stakeholders.
• Collaborate with hunt, reverse-engineering, and DevSecOps teams to operationalize model outputs in high-tempo environments.
• Document experiments thoroughly; champion reproducible research and secure-development principles.