AI Forward Deployed Engineer (FDE)
- Translate business and cybersecurity needs into high-quality technical solutions (including AI/GenAI-enabled capabilities where applicable), and deploy them for clients in alignment with their requirements and architecture
- Identify, troubleshoot, and resolve technical issues within scope
- Partner with stakeholders to help shape technical solutions during pursuits, including leading demos/POCs/prototypes
- Contribute to solution design, ensuring alignment between client needs and delivery feasibility.
- Partner with leadership to influence technical win strategy, including pricing inputs, and effort estimation, ensuring alignment between client needs, feasibility, and commercial viability.
- Own overall customer satisfaction, proactively managing expectations, delivery quality, and outcomes to ensure a strong client experience.
- Build and maintain trusted client relationships, addressing escalations effectively and driving continuous improvement based on client feedback.
- Ensure delivery aligns with agreed scope, timelines, and cost estimates, proactively managing deviations to protect margins and client outcomes.
- Drive delivery efficiency and unit economics by optimizing team structure, utilization, and effort allocation across engagements
- Capture and translate delivery learnings into actionable product or platform improvements, distinguishing reusable enhancements from client-specific needs.
- Enable client teams through structured knowledge transfer, clear documentation, and practical runbooks.
- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field.
- Limited immigration sponsorship may be available.
- Ability to travel up to 20% on average, based on the work you do and the clients and industries/sectors you serve.
- Familiarity with cybersecurity concepts (e.g., application security, cloud security, identity, detection engineering).
- Experience with DevSecOps practices and secure software development lifecycle.
- Exposure to search/analytics platforms (Elasticsearch/OpenSearch) or graph databases (Neo4j).
- Knowledge of Kubernetes, GitOps, or advanced cloud-native patterns.
- Cloud certifications (AWS, Azure, GCP).
- Basic familiarity with ML frameworks (TensorFlow, PyTorch ).
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