The
AI/ML Integration Tech Lead bridges long-term strategy, technical execution, and cross-organizational governance to scale AI capabilities across petabyte-scale cyber defense data pipelines. The role drives the evolution of the Defensive Cyberspace Operations (DCO) platform, establishing robust agentic frameworks, rigorous harness evaluations, and secure lifecycle governance across operational mission systems. Beyond primary program delivery, position will also provide support to corporate AI initiatives, applying technical expertise to support 1-2 corporate initiatives per year.
Key Responsibilities -Strategic & Architectural Leadership- Long-Term Vision: Define the multi-year AI/ML technical roadmap to transition petabyte-scale cyber data (logs, network telemetry, threat intelligence) from passive monitoring to real-time predictive defense.
- Architecture Oversight: Architect scalable, low-latency machine learning pipelines (real-time streaming and batch) capable of handling massive telemetry throughput for threat detection, anomaly scoring, and automated incident response.
- Prototyping & Innovation: Rapidly prototype novel AI/ML concepts, custom models, and agentic workflows to validate emerging defense capabilities before operational scaling.
- Agentic Architecture & Harness Assessment: Maintain deep technical mastery of emerging agentic workflows, multi-agent patterns, orchestration mechanisms, memory boundaries, and tool-use execution harnesses. Assess commercial and open-source agentic solutions for operational viability, architectural feasibility, systemic risk, and scalability within Defensive Cyberspace Operations (DCO) mission workflows, ensuring deterministic controls and human-in-the-loop oversight are preserved.
Vendor Evaluation & Product Integration- Market Evaluation: Conduct technical assessments of 3rd-party vendor products, evaluation of model performance (precision/recall on cyber data), and bench-testing in sandbox environments.
- Integration Engineering: Design standard integration hooks, APIs, and data ingestion paths to safely dock vendor applications directly into the core big data infrastructure.
- DCO Platform Integration & Agentic Evaluation: Define architectural, interface, telemetry, identity, and access-control requirements for integrating AI/ML and agentic capabilities into the DCO Platform, Big Data Platform, and SOC/SIEM/SOAR pipelines. Establish rigorous evaluation criteria and test harness standards to benchmark model groundedness, tool-use reliability, latency, resource consumption, fail-safe isolation, and verifiable audit logging.
- Product Testing: Establish rigorous testing protocols to validate vendor claims regarding accuracy, resource consumption, and false-positive rates under actual operational data loads.
Security, Compliance & Authorization (ATO)- ATO Process Management: Lead technical documentation, security controls implementation, and threat modeling required to navigate the Authority to Operate (ATO) process for new AI models and software.
- Data & Model Security: Enforce secure AI practices across data pipelines, including data anonymization, model robustness testing (protection against adversarial manipulation), and access controls.
- AI/Agent Security & Lifecycle Governance: Establish system security, compliance, and risk-mitigation requirements for AI models, prompts, retrieval stores, orchestration logic, and external tool integrations. Define defenses against prompt injection, retrieval poisoning, data tampering, configuration drift, and model supply-chain vulnerabilities, while governing configuration management, auditing, and retirement standards consistent with DoD AI Ethics and RMF protocols.
- Pipeline Integration: Partner with security engineers to integrate real-time model inference safely into existing Security Operations Center (SOC) workflows and SIEM/SOAR platforms
Stakeholder Alignment & Requirement Engineering- Cross-Organizational Coordination: Serve as the primary technical liaison between cyber analysts, data platform teams, leadership, and external partners.
- Requirement Translation: Translate high-level operational security needs (e.g., reducing analyst triage fatigue, detecting zero-day threats) into clear data architecture and machine learning technical requirements.
- DCO Mission Translation & Technical Advisory: Translate operational DCO mission objectives and analyst workflows into technical specifications for AI/ML and agentic integrations, data readiness, and verification guardrails. Serve as primary technical advisor to leadership, cyber operators, and external vendors regarding architectural fit, operational utility, system boundaries, and mission risk.
Required Qualifications-
- 8+ years of relevant experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- Demonstrated ability to design, build, and deliver production ML models and components at scale that solve real-world operational problems in collaboration with cross-functional engineering teams.
- Strong engineering and problem-solving skills with demonstrated capability to retrain, maintain, and monitor models in production environments.
- Strong consulting skills, with the ability to advise Government leadership on whether 3rd-party vendor platforms and delivered code are "fit for purpose."
- Demonstrated technical understanding of AI/ML architectures, agentic frameworks, execution harnesses, LLMOps, AI security vectors, and integration standards for secure enterprise environments.
- Track record of ensuring code is well-managed to reduce vulnerabilities and models are governed from a risk perspective; proficiency in programming languages such as Python, Scala, or Java.
- Proven ability to mentor and coach junior engineers and technical staff with delivery tasks.
- Self-motivated, with the ability to work effectively both independently and in a team environment
Preferred Qualifications-
- Certifications in Machine Learning / Artificial Intelligence / Data Science
- Familiarity with agile development methodologies and CI/CD pipelines in DoD environments.
- 5-8 years of experience in an ML/AI development and/or technical leadership role.
- Direct experience assessing, piloting, or integrating AI/ML and agentic capabilities with cyber-defense platforms, security telemetry, SIEM/SOAR workflows, or related DoD/IC mission systems.
Education: Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field from accredited institution or Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field from accredited institution.
Clearance: US Citizenship and an Active Top Secret Security Clearance - Mandatory
Salary: $155,000 - $225,000
