Teams at AIFT

Lead machine learning engineering for GenAI security products, translating security research into production models. Responsibilities include LLM fine-tuning, multilingual and multimodal model development, MLOps pipeline optimization, data versioning, monitoring, platform integration, GPU resource management, and mentoring engineers. The role also communicates technical trade-offs, infrastructure costs, latency, and model performance to executives and clients.

Operates and improves cloud and Kubernetes security services, including SIEM integrations, asset discovery, security posture assessments, IAM, network controls, secrets, encryption, vulnerability remediation, detection tuning, incident support, and automation. The role establishes reliability objectives, monitors security pipelines, implements infrastructure-as-code controls, investigates incidents, validates remediation, and coordinates cloud security improvements across teams.

Develop and deploy production-ready Generative AI and LLM applications, including RAG systems, agents, APIs, backend services, and data pipelines. Evaluate models and frameworks, implement monitoring and quality improvements, and integrate AI into products and business workflows. Collaborate with technical and business stakeholders to identify opportunities, rapidly prototype solutions, and scale successful AI systems while optimizing reliability, latency, scalability, and cost.