Job Purpose/Summary
The AI/ML Engineering Lead and Systems Architect is the line manager, mentor, and technical home for Knowmadics' data science and machine learning engineering team. The role is expected to oversee up to six Data Scientists, ML Engineers, and related specialists who are commonly embedded or tasked to specific products and customer-funded programs. Product and program leaders own the outcomes and priorities of those efforts; this Lead owns the people, capability development, technical coherence, and sustainable allocation of the AI/ML team.
Reporting to the CPTO, this is a hands-on player-coach role. The Lead directly contributes architecture, technical spikes, prototypes, model and data decisions, evaluation plans, and early implementation during the formative stages of programs. A central purpose of the role is to collaborate with software, platform, data, and DevSecOps engineers early enough that AI/ML capabilities are built on sound data contracts, runtime patterns, security controls, deployment pathways, and operational assumptions rather than being added after core platform choices have already hardened.
The role owns and evolves the AI/ML reference architecture for applied research products where AI/ML is central to the value proposition, including Counter-UAS, space domain awareness and satellite behavioral analytics, geospatial intelligence from distributed sensor networks, and adjacent Electronic Warfare capabilities. The Lead also guides internal AI tooling for engineering productivity, evaluating when Knowmadics should build, buy, or integrate secure AI harnesses around software-development-life-cycle workflows and how those tools should be governed and measured.
Success in this role looks like AI/ML practitioners who receive consistent coaching even while embedded across products, reusable architecture that reduces reinvention, early technical decisions that survive transition to production, models that perform under operational stress, and internal AI tools that produce measurable improvements without creating unreviewed, insecure, or fragile workflows.
Duties and Responsibilities
People and Team Leadership
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Serve as the formal line manager for up to six DataScientists, ML Engineers,MLOpsEngineers, or related specialists; own coaching, performance management, development, hiring, and succession.
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Allocate staff across product and program teams, clarify priorities and expected outcomes, manage workload and context switching, and remain accountable for employees embedded in matrixed teams.
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Build an AI/ML community of practice through model and code reviews, shared patterns, reusable components, internal learning, and strong experimental and software engineering discipline.
Architecture, Delivery, and Governance
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Own and evolve AI/ML reference architectures and the technical roadmap for applied research products, covering data, training, evaluation, deployment, monitoring, and lifecycle governance.
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Contribute directly during early program formation through architecture decisions, technical spikes, prototypes, and risk-reduction experiments in partnership with platform, software, data, security, and DevSecOps engineers.
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Translate mission workflows into data strategies, model choices, evaluation criteria, data contracts, runtime patterns, and transition-to-production plans; make explicit build, buy, partner, and reuse recommendations.
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Establish practical standards for reproducibility, MLOps, model evaluation, Responsible AI, security, traceability, human oversight, edge and DDIL performance, monitoring, and rollback.
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Lead architecture and model reviews, go/no-go recommendations, and root-cause analysis; identify data, performance, integration, sustainment, and security risks early.
Internal AI Tooling and Business Support
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Define and execute an internal AI productivity roadmap for software-development-life-cycle workflows; evaluate build-versus-buy options, pilot secure AI harnesses, and establish access, data, intellectual-property, audit, and human-review controls.
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Measure adoption and impact on speed, quality, rework, and security; scale only workflows that produce demonstrable value and keep AI-generated work understandable, reviewable, attributable, and owned by employees.
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Support proposal capture, customer discovery, estimates, program reviews, partner engagements, and clear communication of AI capabilities, limitations, uncertainty, and operational tradeoffs.
Qualifications
Required
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8+ years of progressive experience designing, deploying, and operating AI/ML systems, including at least 3 years in technical leadership or architecture and 2+ years in people or team leadership.
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The stated experience and education requirements describe the typical path to this level and are not a gate. Knowmadics will consider an equivalent combination of education, professional experience, and demonstrated skill, and makes exceptions for candidates who can evidence the scope, complexity, and judgment the role requires.
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Eligible to obtain a U.S. Security Clearance - U.S. Citizenship required. An active DoD Secret or higher clearance is preferred; ability to obtain TS/SCI is strongly preferred.
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Bachelor's degree in Computer Science, Data Science, Electrical or Computer Engineering, Applied Mathematics, Physics, Aerospace Engineering, ora related STEM field; advanced degree preferred.
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Demonstrated end-to-end ownership of production AI/ML systems and success coaching and allocating technical employees across matrixed product or program teams.
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Strong hands-on capability in Python and frameworks such as PyTorchor TensorFlow, with practical experience in model packaging, experiment tracking, registries, feature or vector stores, and MLOps platforms.
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Strong systems architecture fundamentals across containers, Kubernetes or comparable orchestration, APIs, event-driven or streaming systems, CI/CD, observability, secure delivery, and production support.
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Experience designing for latency-sensitive, resource-constrained, disconnected, or otherwise operationally demanding environments, with disciplined model evaluation, governance, traceability, safety, and rollback.
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Excellent written and verbal communication skills, including architecture documentation and briefings for engineering, executive, operator, and customer audiences.
Strongly Preferred
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Experience applying AI/ML to Counter-UAS, RF or electronic warfare, space domain awareness, multi-sensor fusion, computer vision, anomaly detection, geospatial intelligence, or related sensor data.
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Experience with DoD open-architecture and interoperability expectations, tactical edge hardware, government programs, or startup and scale-up product environments.
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Practical experience with generative AI, retrieval-augmented generation, coding assistants, agentic workflows, model gateways, and automated evaluation harnesses for internal productivity.
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Active DoD Secret, Top Secret, or TS/SCI clearance.
Working conditions
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Employees may be called upon to participate in in-person meetings, trainings, or company functions at Knowmadics offices or other designated locations. Travel in support of business operations may also be required, and employees are expected to comply with these obligations as part of their position.
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Remote, with preference given to candidates located within approximately 40 miles of Wichita, KS; Lawton, OK; or Round Rock, TX. Relocation assistance may be negotiable for the right candidate.
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Estimated travel: 10-25 percent to customer sites, government program reviews, partner facilities, demonstrations and exercises, industry events, and Knowmadics offices.
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Some weekend or off-hours work may be required to support exercise windows, customer demonstrations, deployment cutovers, incident investigation, or operational deadlines.
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Standard business hours with flexibility to support distributed teams and customers across U.S. time zones.
Physical requirements
May include sitting or standing for extended periods, working with computers and technical equipment, occasional lifting or moving of materials and equipment up to 35 lbs, and travel to field, range, and demonstration environments where conditions may include outdoor weather, noise, and operational tempo typical of defense exercises.
Direct reports
Up to six Data Scientists, ML Engineers, MLOps Engineers, or related AI/ML specialists; the current expected team is approximately five people. Team members may be embedded in or tasked to specific products and programs, but this role remains their formal line manager and is accountable for performance, growth, workload, technical development, and sustainable allocation.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
