Sr. Engineering Manager, AI/ML Cloud Security

Job Description

What's in it for you

We are seeking an experienced Senior Manager to lead and drive initiatives at the intersection of AI/ML, cloud security, data engineering, and network security. This role combines leadership, collaboration, and technical expertise to design innovative AI-driven solutions, manage high-impact projects, and guide teams in solving complex security challenges in cloud environments. You will be instrumental in ensuring project success, driving cross-functional collaboration, and aligning solutions with business goals.

What you will be doing

- Lead cross-functional teams in the design, development, and deployment of AI/ML models for threat detection, anomaly detection, and predictive analytics in cloud and network security.
- Oversee the architecture and implementation of scalable data pipelines for processing large-scale datasets from logs, network traffic, and cloud environments.
- Manage the application of MLOps best practices to ensure efficient deployment and monitoring of machine learning models in production.
- Collaborate with cloud architects, security analysts, and other stakeholders to deliver cloud-native security solutions leveraging platforms like AWS, Azure, or GCP.
- Guide the development of Retrieval-Augmented Generation (RAG) systems, integrating large language models (LLMs) with vector databases to enable real-time, context-aware applications.
- Establish strong project management frameworks to ensure timely delivery of high-quality solutions that align with organizational objectives.
- Analyze network traffic, log data, and telemetry to identify and mitigate cybersecurity threats.
- Ensure data quality, integrity, and compliance with industry standards and regulations such as GDPR, HIPAA, or SOC 2.
- Foster a culture of innovation by driving the integration of the latest AI/ML techniques into security products and services.
- Act as a mentor and coach for technical staff, developing talent within the team and promoting a collaborative work environment.

Required Skills and Experience

AI/ML Expertise:

- Proficiency in advanced machine learning techniques, including neural networks (e.g., CNNs, Transformers) and anomaly detection.
- Experience with AI frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Strong understanding of MLOps tools and practices (e.g., MLflow, Kubeflow).
- Demonstrated experience building and deploying RAG systems integrating LLMs and vector databases for real-world applications.

Data Engineering:

- Expertise in designing and optimizing ETL/ELT pipelines for processing large-scale data.
- Hands-on experience with big data technologies such as Apache Spark, Kafka, and Flink.
- Proficiency in relational and non-relational databases, including ClickHouse and BigQuery.
- Familiarity with vector databases like Pinecone and PGVector, and their application in RAG systems.
- Experience with cloud-native data tools such as AWS Glue, BigQuery, or Snowflake.

Cloud and Security Knowledge:

- Deep understanding of cloud platforms (AWS, Azure, GCP) and their security services.
- Expertise in network security concepts, extended detection and response (XDR), and threat modeling.

Software Engineering:

- Proficiency in programming languages such as Python, Java, or Scala for building data and ML solutions.
- Proven expertise in designing scalable systems and optimizing performance for high-throughput applications.

Leadership, Collaboration, and Project Management:

- Proven ability to lead and manage cross-functional teams, fostering collaboration across engineering, product, and security functions.
- Experience in translating complex technical requirements into actionable project plans, managing timelines, resources, and risks effectively.
- Strong interpersonal and communication skills to present technical concepts clearly to stakeholders at all levels.
- Ability to influence and align stakeholders across teams and drive consensus on technical and strategic decisions.

Preferred Qualifications

- Hands-on experience in cybersecurity, particularly in incident response or threat hunting.
- Familiarity with SIEM platforms and cybersecurity compliance standards such as GDPR or SOC 2.
- Contributions to research, patents, or publications in AI/ML, RAG systems, or cybersecurity.

Education

- BSCS or equivalent required, MSCS or equivalent strongly preferred.

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