Senior Lead AI/ML Engineer - R01567102

Primary Skills

• Value Quantification: Pre-Model Development
• Model Provisioning: Kubernetes, Kibana
• Model Monitoring
• Cloud Computing
• Python/PySpark
• SAS/SPSS
• Great Expectation
• Evidently AI
• Deployment Strategies (A/B, Blue green, Canary)
• Model Testing
• Tools (KubeFlow, BentoML)
• Integration Testing
• ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet)
• Value Quantification: Post-Model Deployment
• Model Experimentation
• R/R Studio

Specialization

ML Engineering: AI/ML Engineer

Job Requirements

Senior Lead AI/Full Stack Engineer

Work Type: Hybrid model with minimum 2–3 days per week.

Description

The mission is to build AI-native tooling that enables rapid, machine-speed response to security threats by implementing work-focused security agents and deterministic orchestration integrated with vulnerability-response stacks (SIEM/SOAR/EDR, identity, vulnerability management, CMDB, ITSM). This is a hands-on engineering role focused on writing production code, operating agent fleets, and delivering across the full SDLC.

Responsibilities

• Write production-grade code, primarily in Go, building deterministic and testable orchestration tooling.
• Operate multi-agent workflows, decomposing backlogs into parallel streams and managing review gates.
• Embed security into development with TDD, validation stages, persona-based threat judging, signed builds, SBOMs, and strict access control.
• Integrate natively with Azure and GitHub Copilot; build missing components within the existing stack.
• Run model-agnostic personas across Claude, Copilot, and ChatGPT, managing token usage and context budgets.
• Own the full SDLC: design, development, testing, integration, deployment, documentation, and knowledge transfer.

Must-have

• Senior engineer actively delivering hands-on production code.
• Deep Azure and GitHub Copilot delivery experience.
• Strong Go or equivalent systems language expertise.
• Proven experience operating multi-agent or multi-model workflows in production.
• Strong secure SDLC practices, TDD discipline, and threat modeling experience.
• End-to-end SDLC ownership experience.

Nice-to-have

• Experience integrating SIEM, SOAR, EDR, identity, vulnerability management, CMDB, or ITSM systems.
• Experience with evaluation harnesses, red-teaming, agent guardrails, sandboxing, reversibility, confidence thresholds, and audit trails.