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Staff AI API Engineer
Company | SentinelOne |
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Location | United States |
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Salary | $170200 – $234600 |
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Type | Full-Time |
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Degrees | Bachelor’s |
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Experience Level | Senior, Expert or higher |
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Requirements
- A degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
- 7+ years of professional experience developing scalable, production-quality Python backend services.
- Expert-level proficiency with Python, asyncio, and backend API frameworks such as FastAPI, gRPC, and GraphQL.
- Deep understanding of backend architectures, distributed systems, and microservices.
- Experience managing database architectures (relational databases, NoSQL, Redis).
- Strong expertise in authentication and authorization protocols (OAuth, JWT, OpenID Connect, etc.).
- Proven track record interacting with third-party APIs and developing fail-safe integrations.
- Familiarity with modern generative AI technologies (e.g., OpenAI, Anthropic, Google Gemini, Meta’s LLaMA) and experience building backend integrations.
- Excellent communication skills and a collaborative approach in globally distributed teams.
Responsibilities
- Develop and maintain high-performance Python-based backend APIs supporting our generative and agentic AI products.
- Design and implement scalable, secure, and reliable backend architectures, leveraging technologies like asyncio, FastAPI, gRPC, Kafka, and GraphQL.
- Ensure robust data management, incorporating relational and NoSQL databases, Redis caching, streaming, and efficient data handling.
- Implement and manage secure zero-trust authentication and authorization schemes for public and internal APIs.
- Collaborate closely with frontend engineers, AI researchers, product managers, and DevOps teams to deliver cohesive product experiences.
- Optimize backend services for scalability, reliability, and performance in high-availability environments.
Preferred Qualifications
- Proficiency in languages beyond Python, including Go, Java, Kotlin, Rust, C++.
- MLOps and AIOps practices and tools such as MLFlow, Airflow, or Weights & Biases.
- Experience with cloud infrastructure (AWS, Azure, GCP) and deployment tools (Docker, Kubernetes, Terraform, ArgoCD).
- Experience optimizing API performance and scalability in production environments.
- Background in cybersecurity, threat detection, or related fields.
- Agile methodologies, including experience as a Technical Lead or Scrum Master.