Senior Software Engineer

AI Systems, Models & Platform Engineering Design and build multi-agent AI systems leveraging LLMs, RAG pipelines, and vector-based retrieval systems to operationalize customer readiness across security domains. Develop and productionize machine learning and deep learning models that transform large-scale, multi-source enterprise signals into contextual intelligence and automation. Implement optimization and automation algorithms for prediction, prioritization, and decision-making across AI readiness workflows. Lead end-to-end delivery of complex AI and security initiatives, from architecture through production readiness and operational scale. Build telemetry, instrumentation, and analytics to track adoption, system performance, and business impact. Drive data-informed decision-making, converting system signals into actionable insights and continuous improvements. Establish governance, documentation, and engineering standards to ensure maintainability, transparency, and reproducibility of AI systems. Required Bachelor's Degree AND 4+ years of experience in AI engineering, system design, or data engineering. Hands-on experience designing and deploying production-grade AI/ML systems, including LLM-based or agentic systems. Strong programming skills (e.g., Python) for model development, data pipelines, and system integration. Experience building and operating distributed systems and scalable data/ML pipelines. 8+ years of experience in AI/ML engineering and large-scale distributed systems. Deep experience with LLMs, RAG architectures, vector databases, and agentic workflows. Expertise in MLOps (CI/CD for ML, model monitoring, versioning, containerization) and production deployment. Strong understanding of statistics, optimization, and machine learning fundamentals. Experience building enterprise-grade AI systems on cloud platforms (Azure preferred). Proven ability to operate in ambiguous, cross-org environments and deliver end-to-end systems. Strong communication skills to translate complex AI systems into clear business and executive insights. Demonstrated leadership in AI adoption, platform building, or security domains. Build and operate scalable data pipelines, ETL workflows, and training infrastructure to support AI lifecycle management. Establish best practices for model governance, evaluation, and lifecycle management aligned with enterprise security and compliance requirements. Builder Mindset & Iteration Velocity Operate in ambiguous environments, converting problem spaces into working AI systems using iterative development, experimentation, and telemetry-driven refinement.