Senior Software Platform Engineer
New
T
TetraScienceLife Sciences / AI
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSDockerPythonTypeScriptCI/CDCloudFormationMLOps
Requirements
- 7+ years of professional experience in software engineering and infrastructure engineering.
- Extensive experience building and maintaining AI/ML infrastructure in production, including model, deployment, and lifecycle management.
- Expert-level coding skills in TypeScript and Python building robust APIs and backend services.
- Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows.
- Expert level understanding of containerization (Docker).
- Hands on experience with CI/CD pipelines and orchestration tools (e.g., ECS).
- Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads.
- Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK.
- Ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members.
- Strong collaboration skills and the ability to partner effectively with cross-functional teams.
Responsibilities
- Design, implement, and maintain cloud-native platform to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock.
- Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics.
- Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments.
- Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production.
- Drive best practices for observability, including monitoring, alerting, and logging for AI platforms.
- Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types.
- Stay current with new tools and technologies to recommend improvements to architecture and operations.
- Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG).
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