AIFT is hiring a Machine Learning Engineer Lead to serve as the engineering architect behind our core AI security capabilities. In this role, you will translate advanced GenAI threat findings into production-ready models for security guardrails and vulnerability assessment. You will bridge deep technology and business strategy, articulating technical constraints to leadership and guiding engineering direction.

What You'll Do

Collaborate with the Security Research Team to operationalize new threat detection techniques, determining model architecture and training strategy.
Lead the fine-tuning of Language Models using techniques like LoRA/PEFT to optimize for multi-lingual languages and specific security intents.
Prepare the system for Multimodal (Text + Image/Audio) capabilities by evaluating and implementing models to detect visual prompt injections and non-textual threats.
Take ownership of existing ML pipelines, optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity.
Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure reproducibility of model artifacts and datasets.
Maintain rigorous monitoring for model drift and performance to ensure high reliability in a production security environment.
Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture.
Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
Manage GPU resources and compute budgets effectively for both training and inference workloads.
Act as the technical voice of the ML team, explaining complex ML concepts (e.g., FLOPS, quantization trade-offs, latency vs. accuracy) to executive leadership and clients.
Justify compute resource investments and articulate the trade-off between infrastructure costs and performance gains to non-technical stakeholders.

What We're Looking For

5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
Proficient in Python, Docker, and Kubernetes. Treat ML models as software artifacts that need testing and version control.
Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
Experience processing or fine-tuning models for multi-lingual environments.