AIFT - Machine Learning Engineer Lead, Vulcan
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Requirements
• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers. • Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers. • Communication & Business Acumen: • MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.). • MLOps Proficiency: • optimizing • Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control. • Engineering First: • NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM. • NLP & LLM Expertise: • Language Support: Experience processing or fine-tuning models for multi-lingual environments. • Language Support: • Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA). • Multimodal Expertise: • Multimodal models • Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection). • Security Awareness: • High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications. • High-Performance Computing: • Vector Database: Experience with Vector DBs for RAG applications. • Vector Database:
Responsibilities
• 1. Model Development & Optimization (Training & Fine-tuning): • Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). • Research to Production: • Security Research Team • Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents. • Fine-tuning & Adaptation: • Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. • Multimodal Readiness: • Multimodal (Text + Image/Audio) • 2. MLOps& Data Infrastructure: • Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. • Enhance & Scale MLOps: • optimizing • Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets. • Data Governance: • Data Versioning • Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment. • Monitoring & Reliability: • 3. Cross-Functional Implementation & Leadership: • Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. • Platform Collaboration: • Platform Engineering Team • Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence. • Team Leadership: • Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. • Resource Management: • 4. Technical Strategy & Stakeholder Management: • Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. • Translating Tech to Business • FLOPS, • executive leadership and clients. • Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. • Cost-Benefit Analysis:
Benefits
• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. • Innovative Environment: • Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. • Growth Opportunities: • Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. • Dynamic Team: • Compensation: Competitive salary and benefits package, commensurate with experience and performance. • Application Process • Application Process • If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying. • If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn.
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