Engineering Team Leader - AI Security

● Lead, grow, mentor, and manage a team of talented software engineers, ensuring the delivery of high-quality features and products.

● Develop and maintain both the back-end and front-end of DeepKeep’s core applications, contributing as a hands-on developer for 50%-70%

of your time.

● Translate advanced research prototypes into scalable, production-grade software, optimizing machine learning models for performance and security

(e.g., early stopping, adversarial attack defenses).

● Work closely with cross-functional teams including data scientists, product managers, and machine learning engineers to ensure smooth integration

of ML technologies.

● Drive technical excellence and innovation, optimizing distributed AWS systems, serverless architectures, and upholding best practices in code quality.

● Participate in product design and decision-making processes, focusing on continuous improvement, scalability, and user-centric designs.

Who We're Looking For:

● A visionary leader with a passion for AI and machine learning technologies, capable of bridging the gap between research and real-world applications.

● Strong leadership with 2+ years as a team leader managing 5+ engineers, driving both technical and personal growth within the team.

● Substantial experience (6+ years) in software development, including python ML technologies and backend technologies.

● Proven ability to transform complex research and algorithms into scalable, market-ready solutions.

● Expertise in machine learning frameworks (e.g., PyTorch, TensorFlow), with a deep understanding of MLOps practices and large-scale ML systems.

● Familiarity with distributed systems, serverless architectures, cloud environments (AWS, Azure, GCP), and coding standards.

Qualifications:

● 2+ years as a team leader, managing and mentoring a software engineering team in an agile, production-critical environment.

● 6+ years of development experience, including strong knowledge of front-end frameworks and backend architectures.

● Experience in distributed AWS systems architecture, serverless solutions, and CI/CD pipelines.

● Hands-on experience with machine learning technologies, MLOps, and integrating ML models into production.

● Exceptional Python coding skills, with familiarity in APIs, Kafka, SQL, NoSQL, and strictly typed languages being advantageous.

● Knowledge of machine learning databases, vector databases, and optimization against adversarial attacks is a bonus.