AIFT
MachineLearningEngineerLead
Neural analysis suggests this role is
optimal for Lead candidates.
“Machine Learning Engineer Lead at AIFT. Skills: Machine Learning Lead, AI capabilities, GenAI Security Guardrails, Automated Vulnerability Assessment, LLM fine-tuning, MLOps, Data Versioning, Model monitoring, Platform integration, Team Leadership. Determine model architecture and training strategy. Lead fine-tuning of Language Models”
What You'll Achieve.
Translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models; Power GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team); Improve training efficiency and deployment velocity; Ensure complete reproducibility of model artifacts and datasets; Ensure high reliability in a production security environment; Ensure seamless interaction between model inference services and the main platform logic; Foster a culture of engineering rigor, code quality, and operational excellence
Industry & Context.
Optimize model performance; Ensure model reliability; Address technical constraints
What They're Looking For.
Must Have
5+ years in Machine Learning Engineering, Experience in leading technical projects or mentoring engineers, Proficient in Python, Proficient in Docker, Proficient in Kubernetes, Experience with Transformer architectures, Experience with Embeddings, Experience with LLM fine-tuning, Experience processing or fine-tuning models for multi-lingual environments
Nice to Have
Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA), Understanding of GenAI security threats (e.g., Prompt Injection), Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications, Experience with Vector DBs for RAG applications
What You'll Do.
Determine model architecture and training strategy
Lead fine-tuning of Language Models
Optimize for multi-lingual languages and specific security intents
Prepare system for Multimodal capabilities
Evaluate and implement models for visual prompt injections and non-textual threats
Enhance and scale MLOps
Optimize and scale CI/CD/CT workflows
Improve training efficiency and deployment velocity
Implement and enforce Data Versioning strategies
Ensure reproducibility of model artifacts and datasets
Maintain monitoring for model drift and performance
Ensure high reliability in a production security environment
Integrate ML models into the broader product architecture
Ensure seamless interaction between model inference services and the main platform logic
Lead and mentor Machine Learning Engineers
Manage GPU resources and compute budgets
How You'll Work.
Team & Collaboration
Act as the nexus between Research, Platform, and Product; Work closely with the Platform Engineering Team; Explain complex ML concepts to executive leadership and clients; Articulate trade-offs between infrastructure costs and performance gains to non-technical stakeholders
Communication Scope
Exceptional ability to distill complex technical topics into clear, business-relevant insights; Effectively explain complex ML concepts to executive leadership and clients; Articulate technical constraints to leadership and clients
Process & Methodology
Leading technical projects
Full Job Description
About the role We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. Key Responsibilities 1. Model Development you determine the "how" (model architecture, training strategy). 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. 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. 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. Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets. Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment. 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. Team Leadership: Lead and me
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