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.

Sr. Engineering Manager, AI/ML Cloud Security & Data Engineering