Principal AI Engineer
New
Fully remote work within the United StatesFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSDockerPythonSQLMachine LearningNosqlLLMMLOpsDistributed Systems
Requirements
- 7+ years of experience in AI/ML engineering with strong production-level experience.
- Advanced proficiency in Python and strong software engineering fundamentals, including system and API design.
- Deep experience with distributed systems, event-driven architectures, and cloud-native engineering patterns.
- Strong hands-on expertise with LLM systems including prompt engineering, tool/function calling, RAG architectures, embeddings, vector databases, and multi-agent systems.
- Proven experience building and operating MLOps pipelines, including deployment, monitoring, versioning, and reproducibility.
- Strong experience with AWS, including GenAI services such as AWS Bedrock, and familiarity with other cloud platforms.
- Experience working with both SQL and NoSQL databases and designing scalable data architectures.
- Familiarity with containerization technologies such as Docker and modern CI/CD practices.
- Strong communication skills with the ability to translate complex AI concepts into business and technical decisions.
- Experience integrating AI systems with enterprise tools, APIs, or workflow platforms (e.g., ServiceNow, Jira).
- Exposure to AI governance, security, and compliance considerations in production environments.
Responsibilities
- Define and lead the AI system architecture and technical strategy across the full lifecycle, from design through production deployment.
- Design and build scalable ML platforms, pipelines, and event-driven systems supporting distributed and asynchronous workloads.
- Architect and implement LLM-based systems including RAG pipelines, embeddings, vector databases, prompt engineering, and multi-agent orchestration.
- Develop and maintain backend services and APIs that integrate AI capabilities into enterprise and third-party systems.
- Lead model development, optimization, and productionization of AI solutions, ensuring reliability and scalability in real-world environments.
- Establish engineering standards and best practices for AI development, MLOps, monitoring, and system observability.
- Ensure system performance, security, and governance across all deployed AI solutions.
- Collaborate with product, engineering, and leadership teams to align AI initiatives with business priorities and roadmap execution.
- Mentor engineers and influence technical culture across teams, raising the overall bar for AI engineering excellence.
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