Applied AI ML Lead - ML Assisted Surveillance

We have an exciting opportunity for you to advance your engineering leadership career and drive innovation in ML-powered solutions.

As an Applied AI ML Lead in the Surveillance Product Team, you will guide and coach teams to deliver high-quality, cloud-native, ML-powered business applications. You will foster a culture of engineering excellence and compliance, collaborating across functions to achieve strategic goals.

Job responsibilities

• Coach teams on design rigor, testing strategies, and SRE mindset to ensure operational readiness.
• Drive a culture of ownership, engineering excellence, and compliance-first thinking.
• Own end-to-end delivery of the product roadmap through Agile planning and risk mitigation.

• Build and productionize ML and LLM-based detection and alerting features, ensuring feedback loops for model improvement.
• Partner with Architecture and senior engineers to influence system design and ensure architectural consistency.
• Enforce operational maturity and DevOps culture to eliminate production toil.
• Collaborate cross-functionally with Product, Compliance, Data Science, and SRE teams to ensure risk and operational excellence.

Required qualifications, capabilities, and skills

• 5+years of experience as a Machine Learning Engineer and two years in people management roles.
• Proven experience leading cloud-native, data-intensive product engineering teams.
• Experience building and delivering ML-powered business applications using public cloud providers such as AWS, Azure, or GCP.
• Experience in regulated or compliance-driven domains such as fintech, surveillance, or risk.

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• Strong foundation in Information Retrieval, Natural Language Processing, Classification, and vector similarity search.
• Experience integrating models into cloud-scale, distributed systems and microservices architectures.
• Excellent verbal and written communication skills, able to explain technical and ML concepts to non-technical stakeholders.

Preferred qualifications, capabilities, and skills

• Experience in Data Science and data pipelines.
• Exposure to Data Bricks.
• Familiarity with vector search, embeddings, RAG patterns, model explainability, and auditability concepts.
• Operational experience supporting enterprise-grade ML applications in production.