Machine Learning Engineer Lead, Vulcan (Global)

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).

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 mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.

Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads.

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.

Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders.

Qualifications

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.

MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).

Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.

NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.

Language Support: Experience processing or fine-tuning models for multi-lingual environments.

Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).

Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).

High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.

Vector Database: Experience with Vector DBs for RAG applications.

Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects.

Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture.

Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other .

Compensation : Competitive salary and benefits package, commensurate with experience and performance.