Manager, Engineering - AI and Data Team Manager
New
J
JobgetherCybersecurity AI Data
Based in United StatesFull-TimeManager
Salary$172,000 - $236,500 annually
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Job Details
- Experience
- 5+ years of experience in data science, machine learning engineering, or data engineering, including 2+ years in a technical leadership or team lead role.
- Required Skills
- AWSPythonMachine LearningSoftware ArchitectureData engineeringData scienceLLMMLOps
Requirements
- 5+ years of experience in data science, machine learning engineering, or data engineering.
- 2+ years in a technical leadership or team lead role.
- Strong understanding of LLM technologies, retrieval-augmented generation (RAG), and prompt engineering.
- Extensive experience with Python and AWS services (S3, Lambda, Batch, Glue, Bedrock, Step Functions, Redshift).
- Proven experience building and deploying end-to-end machine learning pipelines.
- Strong knowledge of software architecture principles and scalable data systems.
- Experience designing and managing high-performance data pipelines.
- Excellent communication skills for mentoring and influencing technical decisions.
- Experience with cybersecurity, vulnerability research, or offensive security workflows is highly desirable.
- Familiarity with compliance environments like FedRAMP or SOC 2 is a plus.
- Master’s degree in Computer Science, Information Systems, Engineering, or related discipline preferred.
Responsibilities
- Define and execute the technical roadmap for AI, machine learning, and data systems.
- Lead, mentor, and develop a team of data scientists and ML engineers.
- Oversee the full lifecycle of AI and ML solutions, including architecture, development, deployment, monitoring, and optimization.
- Guide the creation of scalable data pipelines, model training processes, and AI-powered applications.
- Drive the integration of generative AI technologies, including large language models and retrieval-augmented generation architectures.
- Architect and optimize large-scale data platforms for secure processing of complex datasets.
- Establish strong MLOps practices, including CI/CD workflows, model evaluation, and system observability.
- Oversee API design and integrations for AI systems and automated workflows.
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