Principal AI Security Platform & Harness Engineer at The Depository Trust & Clearing Corporation (DTCC) Principal AI Security Platform & Harness Engineer at The Depository Trust & Clearing Corporation (DTCC) in Secaucus, New Jersey Posted in 13 days ago.

Type:

full-time The Impact you will have in this role: Being a member of IT Cybersecurity & Platform Strategy team, you will build and operate the next generation of AI-powered vulnerability discovery capabilities used to identify security weaknesses across thousands of applications and repositories. This role sits at the intersection of Application Security, AI Engineering and Platform Engineering. You will design, develop and scale AI scanning harnesses, agent frameworks and model integrations that enable large-scale security analysis using AI models. Your work will directly influence how DTCC leverages AI to discover exploitable vulnerabilities, validate findings, reduce false positives and accelerate security outcomes across the enterprise.

Your Primary Responsibilities:

Design, build and operate AI-powered vulnerability scanning harnesses and supporting platform capabilities. Develop integrations with large language models and AI platforms. Create and maintain prompt frameworks, agent workflows and security-focused AI orchestration logic. Build scalable scanning infrastructure supporting large-scale repository and application analysis. Optimise harness performance, throughput, cost efficiency and model utilisation. Develop validation, verification and false-positive reduction mechanisms. Integrate AI scanning capabilities into software development and security workflows. Evaluate new AI models, scanning techniques and agentic security approaches. Develop supporting tooling for scan execution, scheduling, orchestration and reporting. Enhance contextual analysis by incorporating security controls, architecture data and remediation intelligence. Work closely with AppSec, Vulnerability Operations and software engineering teams to drive adoption. Contribute to secure platform design, isolation controls, monitoring and operational resilience. Support research and innovation in agentic AI, automated vulnerability discovery and AI-assisted security testing.

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NOTE:

The Primary Responsibilities of this role are not limited to the details above.

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Qualifications:

Minimum of 8 years of related experience Bachelor's degree preferred and/or equivalent experience Talents Needed for

Success:

6+ years of software engineering experience with strong emphasis on Python and distributed systems development Deep expertise building scalable software platforms, test harnesses, frameworks, or orchestration platforms Strong experience integrating Large Language Models through APIs and SDKs Experience building evaluation frameworks, benchmarking systems, automated testing platforms, or AI experimentation environments Experience designing and maintaining multi-model orchestration workflows across Claude, OpenAI, Bedrock, or equivalent platforms Strong experience developing microservices and APIs using Python and/or Java Experience building data processing pipelines, queue-based architectures, and event-driven systems Experience with Docker, Kubernetes, OpenShift, and cloud-native deployments Experience operating applications within AWS environments Experience building automated testing frameworks, regression suites, and validation pipelines Experience handling structured outputs, prompt engineering, model evaluation, and result normalization Experience designing scalable ingestion, correlation, and analysis pipelines Strong understanding of software engineering best practices, testing, observability, and operational excellence Experience with Git, Bitbucket, Jenkins, Jira, and modern CI/CD tooling Preferred Qualifications Experience developing AI scanning harnesses, agent frameworks, or autonomous workflow systems Experience working with security scanning technologies and vulnerability discovery platforms Familiarity with application security concepts and vulnerability management processes Experience designing AI model evaluation and accuracy measurement frameworks