Lead the technical maturation of advanced prototypes into production-ready systems, identifying gaps in architecture, scalability, reliability, and usability.
Define and drive technical roadmaps that align product features with customer requirements.
Identify and implement variability points to enable rapidly tailoring solutions to the needs of different customers.
Establish criteria for MVPs, beta releases, and production readiness.
Lead planning and implementation of system architectures for AI-enabled software systems, including APIs, services, data pipelines, and deployment infrastructure.
Make informed tradeoffs between research flexibility and product stability, performance, and maintainability.
Guide refactoring of research codebases into modular, reusable, and testable components.
Collaborate with UI/UX designers to understand and incorporate end user interaction needs into architecture decisions.
Design and implement workflows for training, evaluation, deployment, and monitoring of AI/ML models.
Establish MLOps practices including versioning, reproducibility, CI/CD, performance monitoring, and lifecycle management.
Work closely with researchers to transition experimental models into operational pipelines.
Lead containerized deployments using Docker and related tooling; support cloud and on-premise environments.
Apply modern DevSecOps practices to improve system reliability, security, observability, monitoring, logging, and operational diagnostics.
Anticipate operational risks and design mitigations for real-world usage.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).
Ability to obtain and maintain a U.S. Government security clearance.
6+ years of professional experience in software engineering, with demonstrated ownership of complex systems.
Strong proficiency in Python and experience with production-quality software development.
Demonstrated experience transitioning prototypes or early-stage systems into production or near-production environments.
Strong understanding of AI/ML systems, including model integration, evaluation, and deployment considerations.
Hands-on experience with containerization (Docker) and modern deployment workflows.
Ability to lead technical planning, break down ambiguous problems, and drive execution across teams.
Strong communication skills and comfort working with researchers, engineers, and external stakeholders.
Willingness to travel to support integration, deployment, or customer activities.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonAI systemsML systemsMLOpscontainerizationDevSecOpsproduction-quality software developmentsystem architecturedata pipelinesAPIs

Soft Skills

leadershipcommunicationproblem-solvingcollaborationtechnical planningexecutionadaptabilityrisk managementcustomer focusinterpersonal skills

Certifications

Bachelor’s degree in Computer ScienceBachelor’s degree in EngineeringBachelor’s degree in Data ScienceU.S. Government security clearance